{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introductory examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.usa.gov data from bit.ly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.444471Z",
     "start_time": "2019-01-19T00:48:05.403128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/Users/thomas_young/Documents/git_download/pydata-book'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.488439Z",
     "start_time": "2019-01-19T00:48:05.447525Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.525917Z",
     "start_time": "2019-01-19T00:48:05.491652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.664047Z",
     "start_time": "2019-01-19T00:48:05.529565Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.706522Z",
     "start_time": "2019-01-19T00:48:05.666742Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'a': u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " u'al': u'en-US,en;q=0.8',\n",
       " u'c': u'US',\n",
       " u'cy': u'Danvers',\n",
       " u'g': u'A6qOVH',\n",
       " u'gr': u'MA',\n",
       " u'h': u'wfLQtf',\n",
       " u'hc': 1331822918,\n",
       " u'hh': u'1.usa.gov',\n",
       " u'l': u'orofrog',\n",
       " u'll': [42.576698, -70.954903],\n",
       " u'nk': 1,\n",
       " u'r': u'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " u't': 1331923247,\n",
       " u'tz': u'America/New_York',\n",
       " u'u': u'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.744709Z",
     "start_time": "2019-01-19T00:48:05.709509Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'America/New_York'"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]['tz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.794910Z",
     "start_time": "2019-01-19T00:48:05.747642Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "America/New_York\n"
     ]
    }
   ],
   "source": [
    "print(records[0]['tz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.866209Z",
     "start_time": "2019-01-19T00:48:05.810359Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "����\n",
      "测试\n"
     ]
    }
   ],
   "source": [
    "uyangkai = u'测试'\n",
    "byangkai = uyangkai.encode('gbk')\n",
    "print(byangkai)\n",
    "print(uyangkai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.926458Z",
     "start_time": "2019-01-19T00:48:05.869552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试\n",
      "����\n"
     ]
    }
   ],
   "source": [
    "print(uyangkai)\n",
    "print(byangkai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:05.974279Z",
     "start_time": "2019-01-19T00:48:05.930909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'\\u6d4b\\u8bd5'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uyangkai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.017730Z",
     "start_time": "2019-01-19T00:48:05.977578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\xb2\\xe2\\xca\\xd4'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byangkai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.077044Z",
     "start_time": "2019-01-19T00:48:06.024360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byangkai.decode('gbk') == uyangkai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.119895Z",
     "start_time": "2019-01-19T00:48:06.080934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "杨恺 a x\n",
      "ascii\n"
     ]
    }
   ],
   "source": [
    "x = '%s a %s' % (u'杨恺'.encode('utf8'), 'x')\n",
    "print(x)\n",
    "import sys\n",
    "print(sys.getdefaultencoding())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting time zones in pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.152425Z",
     "start_time": "2019-01-19T00:48:06.123062Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.188924Z",
     "start_time": "2019-01-19T00:48:06.155462Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.260438Z",
     "start_time": "2019-01-19T00:48:06.196412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'America/New_York',\n",
       " u'America/Denver',\n",
       " u'America/New_York',\n",
       " u'America/Sao_Paulo',\n",
       " u'America/New_York',\n",
       " u'America/New_York',\n",
       " u'Europe/Warsaw',\n",
       " u'',\n",
       " u'',\n",
       " u'']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.299092Z",
     "start_time": "2019-01-19T00:48:06.265445Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.336461Z",
     "start_time": "2019-01-19T00:48:06.301556Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int) # values will initialize to 0\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.373854Z",
     "start_time": "2019-01-19T00:48:06.339446Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = get_counts(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.452241Z",
     "start_time": "2019-01-19T00:48:06.376704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.493437Z",
     "start_time": "2019-01-19T00:48:06.456317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.526754Z",
     "start_time": "2019-01-19T00:48:06.496146Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.595666Z",
     "start_time": "2019-01-19T00:48:06.529173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, u'America/Sao_Paulo'),\n",
       " (35, u'Europe/Madrid'),\n",
       " (36, u'Pacific/Honolulu'),\n",
       " (37, u'Asia/Tokyo'),\n",
       " (74, u'Europe/London'),\n",
       " (191, u'America/Denver'),\n",
       " (382, u'America/Los_Angeles'),\n",
       " (400, u'America/Chicago'),\n",
       " (521, u''),\n",
       " (1251, u'America/New_York')]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.641818Z",
     "start_time": "2019-01-19T00:48:06.601747Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.675629Z",
     "start_time": "2019-01-19T00:48:06.645108Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = Counter(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.728930Z",
     "start_time": "2019-01-19T00:48:06.678368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'America/New_York', 1251),\n",
       " (u'', 521),\n",
       " (u'America/Chicago', 400),\n",
       " (u'America/Los_Angeles', 382),\n",
       " (u'America/Denver', 191),\n",
       " (u'Europe/London', 74),\n",
       " (u'Asia/Tokyo', 37),\n",
       " (u'Pacific/Honolulu', 36),\n",
       " (u'Europe/Madrid', 35),\n",
       " (u'America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting time zones with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.780748Z",
     "start_time": "2019-01-19T00:48:06.733338Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:06.815203Z",
     "start_time": "2019-01-19T00:48:06.783486Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.004330Z",
     "start_time": "2019-01-19T00:48:06.817760Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
    "lines = open(path).readlines()\n",
    "records = [json.loads(line) for line in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.274046Z",
     "start_time": "2019-01-19T00:48:07.008661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>MA</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>1.273673e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n",
       "      <td>pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>PL</td>\n",
       "      <td>Luban</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>77</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[51.116699, 15.2833]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://plus.url.google.com/url?sa=z&amp;n=13319232...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Europe/Warsaw</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...</td>\n",
       "      <td>bg,en-us;q=0.7,en;q=0.3</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...</td>\n",
       "      <td>en-US, en</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/o1Pd0WeV</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>WA</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[47.5951, -122.332603]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>wG7OIH</td>\n",
       "      <td>DC</td>\n",
       "      <td>A0nRz4</td>\n",
       "      <td>1.331816e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>darrellissa</td>\n",
       "      <td>[38.937599, -77.092796]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/ND7SoPyo</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://oversight.house.gov/wp-content/uploads/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Alexandria</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>VA</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[38.790901, -77.094704]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>2rOUYc</td>\n",
       "      <td>GA</td>\n",
       "      <td>2rOUYc</td>\n",
       "      <td>1.255770e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[33.953201, -84.5177]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://toxtown.nlm.nih.gov/index.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>HK</td>\n",
       "      <td>Central District</td>\n",
       "      <td>nQvgJp</td>\n",
       "      <td>00</td>\n",
       "      <td>rtrrth</td>\n",
       "      <td>1.317318e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walkeryuen</td>\n",
       "      <td>[22.2833, 114.150002]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Asia/Hong_Kong</td>\n",
       "      <td>http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>HK</td>\n",
       "      <td>Central District</td>\n",
       "      <td>XdUNr</td>\n",
       "      <td>00</td>\n",
       "      <td>qWkgbq</td>\n",
       "      <td>1.317318e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walkeryuen</td>\n",
       "      <td>[22.2833, 114.150002]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Asia/Hong_Kong</td>\n",
       "      <td>http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Buckfield</td>\n",
       "      <td>zH1BFf</td>\n",
       "      <td>ME</td>\n",
       "      <td>x3jOIv</td>\n",
       "      <td>1.331840e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>andyzieminski</td>\n",
       "      <td>[44.299702, -70.369797]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/6Cx4ROLs</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.usda.gov/wps/portal/usda/usdahome?c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>IT</td>\n",
       "      <td>Venice</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>20</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[45.438599, 12.3267]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Europe/Rome</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>es-ES</td>\n",
       "      <td>ES</td>\n",
       "      <td>Alcal</td>\n",
       "      <td>zQ95Hi</td>\n",
       "      <td>51</td>\n",
       "      <td>ytZYWR</td>\n",
       "      <td>1.331671e+09</td>\n",
       "      <td>bitly.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jplnews</td>\n",
       "      <td>[37.516701, -5.9833]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Africa/Ceuta</td>\n",
       "      <td>http://voyager.jpl.nasa.gov/imagesvideo/uranus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Davidsonville</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>MD</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[38.939201, -76.635002]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Hockessin</td>\n",
       "      <td>y3ZImz</td>\n",
       "      <td>DE</td>\n",
       "      <td>y3ZImz</td>\n",
       "      <td>1.331064e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[39.785, -75.682297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://portal.hud.gov/hudportal/documents/hudd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Lititz</td>\n",
       "      <td>wWiOiD</td>\n",
       "      <td>PA</td>\n",
       "      <td>wWiOiD</td>\n",
       "      <td>1.330218e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.174999, -76.3078]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.tricare.mil/mybenefit/ProfileFilter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES...</td>\n",
       "      <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n",
       "      <td>ES</td>\n",
       "      <td>Bilbao</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>59</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[43.25, -2.9667]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4</td>\n",
       "      <td>MY</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>14</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[3.1667, 101.699997]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>CY</td>\n",
       "      <td>Nicosia</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>04</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[35.166698, 33.366699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/?ref=tn_tnmn</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Asia/Nicosia</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>BR</td>\n",
       "      <td>SPaulo</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.5333, -46.616699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td></td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FPX0IM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FPX0IL</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>twittershare</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://t.co/5xlp0B34</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.ed.gov/news/media-advisories/us-dep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>xVZg4P</td>\n",
       "      <td>CA</td>\n",
       "      <td>wqUkTo</td>\n",
       "      <td>1.331908e+09</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>[37.7645, -122.429398]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2Fg...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.nasa.gov/multimedia/imagegallery/im...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3532</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Au3aUS</td>\n",
       "      <td>DC</td>\n",
       "      <td>A9ct6C</td>\n",
       "      <td>1.331926e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ncsha</td>\n",
       "      <td>[38.904202, -77.031998]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.ncsha.org/</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://portal.hud.gov/hudportal/HUD?src=/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3533</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>b2UtUJ</td>\n",
       "      <td>FL</td>\n",
       "      <td>ieCdgH</td>\n",
       "      <td>1.301393e+09</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>[30.279301, -81.585098]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://apod.nasa.gov/apod/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Frisco</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>TX</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[33.149899, -96.855499]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3535</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Houston</td>\n",
       "      <td>zIgLx8</td>\n",
       "      <td>TX</td>\n",
       "      <td>yrPaLt</td>\n",
       "      <td>1.331903e+09</td>\n",
       "      <td>aash.to</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aashto</td>\n",
       "      <td>[29.775499, -95.415199]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://ntl.bts.gov/lib/44000/44300/44374/FHWA-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e...</td>\n",
       "      <td>en-US,en;q=0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>xIcyim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yG1TTf</td>\n",
       "      <td>1.331728e+09</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/g1VKE8zS</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/hurricanes/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n",
       "      <td>HN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>08</td>\n",
       "      <td>w63FZW</td>\n",
       "      <td>1.331547e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bufferapp</td>\n",
       "      <td>[14.1, -87.216698]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/A8TJyibE</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Tegucigalpa</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>qMac9k</td>\n",
       "      <td>CA</td>\n",
       "      <td>qds1Ge</td>\n",
       "      <td>1.310474e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>healthypeople</td>\n",
       "      <td>[34.041599, -118.298798]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://healthypeople.gov/2020/connect/webinars...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3539</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>zu2M5o</td>\n",
       "      <td>WA</td>\n",
       "      <td>zDhdro</td>\n",
       "      <td>1.331586e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glimtwin</td>\n",
       "      <td>[47.615398, -122.210297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.federalreserve.gov/newsevents/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Payson</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>UT</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[40.014198, -111.738899]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3541</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>zu2M5o</td>\n",
       "      <td>WA</td>\n",
       "      <td>zDhdro</td>\n",
       "      <td>1.331586e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glimtwin</td>\n",
       "      <td>[47.615398, -122.210297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.federalreserve.gov/newsevents/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Pittsburg</td>\n",
       "      <td>y3reI1</td>\n",
       "      <td>CA</td>\n",
       "      <td>y3reI1</td>\n",
       "      <td>1.331926e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.0051, -121.838699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.sba.gov/community/blogs/community-b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Wentzville</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>MO</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[38.790001, -90.854897]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Saint Charles</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>IL</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1.319564e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[41.9352, -88.290901]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>qMac9k</td>\n",
       "      <td>CA</td>\n",
       "      <td>qds1Ge</td>\n",
       "      <td>1.310474e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>healthypeople</td>\n",
       "      <td>[34.041599, -118.298798]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://healthypeople.gov/2020/connect/webinars...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Silver Spring</td>\n",
       "      <td>y0jYkg</td>\n",
       "      <td>MD</td>\n",
       "      <td>y0jYkg</td>\n",
       "      <td>1.331852e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[39.052101, -77.014999]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.epa.gov/otaq/regs/fuels/additive/e1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Mcgehee</td>\n",
       "      <td>y5rMac</td>\n",
       "      <td>AR</td>\n",
       "      <td>xANY6O</td>\n",
       "      <td>1.331916e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>twitterfeed</td>\n",
       "      <td>[33.628399, -91.356903]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://twitter.com/fdarecalls/status/18069759...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://www.fda.gov/Safety/Recalls/ucm296326.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>SE</td>\n",
       "      <td>Sollefte</td>\n",
       "      <td>eH8wu</td>\n",
       "      <td>24</td>\n",
       "      <td>7dtjei</td>\n",
       "      <td>1.260316e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tweetdeckapi</td>\n",
       "      <td>[63.166698, 17.266701]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>Europe/Stockholm</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/WISE/main/in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Conshohocken</td>\n",
       "      <td>A00b72</td>\n",
       "      <td>PA</td>\n",
       "      <td>yGSwzn</td>\n",
       "      <td>1.331918e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>addthis</td>\n",
       "      <td>[40.0798, -75.2855]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.linkedin.com/home?trk=hb_tab_home_top</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.nlm.nih.gov/medlineplus/news/fullst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://plus.url.google.com/url?sa=z&amp;n=13319268...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Decatur</td>\n",
       "      <td>rqgJuE</td>\n",
       "      <td>AL</td>\n",
       "      <td>xcz8vt</td>\n",
       "      <td>1.331227e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bootsnall</td>\n",
       "      <td>[34.572701, -86.940598]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://travel.state.gov/passport/passport_5535...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3553</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>MA</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>1.273673e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>en</td>\n",
       "      <td>US</td>\n",
       "      <td>Paramus</td>\n",
       "      <td>e5SvKE</td>\n",
       "      <td>NJ</td>\n",
       "      <td>fqPSr9</td>\n",
       "      <td>1.301298e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tweetdeckapi</td>\n",
       "      <td>[40.9445, -74.07]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.fda.gov/AdvisoryCommittees/Committe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>OK</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>1.307530e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[35.4715, -97.518997]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://www.okc.gov/PublicNotificationSystem/Fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleProducer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>CA</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>1.327529e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[37.419201, -122.057404]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.ahrq.gov/qual/qitoolkit/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Mc Lean</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>VA</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>1.312898e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.935799, -77.162102]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://t.co/OEEEvwjU</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://herndon-va.gov/Content/public_safety/Pu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3560 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       _heartbeat_                                                  a  \\\n",
       "0              NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1              NaN                             GoogleMaps/RochesterNY   \n",
       "2              NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3              NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4              NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "5              NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "6              NaN  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...   \n",
       "7              NaN  Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...   \n",
       "8              NaN  Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...   \n",
       "9              NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "10             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "11             NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4...   \n",
       "12             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "13    1.331923e+09                                                NaN   \n",
       "14             NaN  Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...   \n",
       "15             NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "16             NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "17             NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r...   \n",
       "18             NaN                             GoogleMaps/RochesterNY   \n",
       "19             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "20             NaN  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "21             NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6...   \n",
       "22             NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "23             NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)...   \n",
       "24             NaN  Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES...   \n",
       "25             NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "26             NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "27             NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "28             NaN  Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)...   \n",
       "29             NaN  Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X...   \n",
       "...            ...                                                ...   \n",
       "3530           NaN  Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1...   \n",
       "3531           NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6...   \n",
       "3532           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3533           NaN  Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A...   \n",
       "3534           NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "3535           NaN  Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/...   \n",
       "3536           NaN  Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e...   \n",
       "3537           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3538           NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3539           NaN    Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE   \n",
       "3540           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3541           NaN  Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;...   \n",
       "3542           NaN  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "3543  1.331927e+09                                                NaN   \n",
       "3544           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ...   \n",
       "3545           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3546           NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3547           NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "3548           NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3549           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3550           NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3551           NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3552           NaN  Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...   \n",
       "3553           NaN  Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...   \n",
       "3554           NaN  Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...   \n",
       "3555           NaN  Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "3556           NaN  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...   \n",
       "3557           NaN                             GoogleMaps/RochesterNY   \n",
       "3558           NaN                                     GoogleProducer   \n",
       "3559           NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "\n",
       "                                          al     c                cy       g  \\\n",
       "0                             en-US,en;q=0.8    US           Danvers  A6qOVH   \n",
       "1                                        NaN    US             Provo  mwszkS   \n",
       "2                                      en-US    US        Washington  xxr3Qb   \n",
       "3                                      pt-br    BR              Braz  zCaLwp   \n",
       "4                             en-US,en;q=0.8    US        Shrewsbury  9b6kNl   \n",
       "5                             en-US,en;q=0.8    US        Shrewsbury  axNK8c   \n",
       "6        pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4    PL             Luban  wcndER   \n",
       "7                    bg,en-us;q=0.7,en;q=0.3  None               NaN  wcndER   \n",
       "8                                  en-US, en  None               NaN  wcndER   \n",
       "9        pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4  None               NaN  zCaLwp   \n",
       "10                            en-us,en;q=0.5    US           Seattle  vNJS4H   \n",
       "11                            en-us,en;q=0.5    US        Washington  wG7OIH   \n",
       "12                            en-us,en;q=0.5    US        Alexandria  vNJS4H   \n",
       "13                                       NaN   NaN               NaN     NaN   \n",
       "14                            en-us,en;q=0.5    US          Marietta  2rOUYc   \n",
       "15       zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4    HK  Central District  nQvgJp   \n",
       "16       zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4    HK  Central District   XdUNr   \n",
       "17                            en-us,en;q=0.5    US         Buckfield  zH1BFf   \n",
       "18                                       NaN    US             Provo  mwszkS   \n",
       "19       it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4    IT            Venice  wcndER   \n",
       "20                                     es-ES    ES             Alcal  zQ95Hi   \n",
       "21                            en-us,en;q=0.5    US     Davidsonville  wcndER   \n",
       "22                                     en-us    US         Hockessin  y3ZImz   \n",
       "23                                     en-us    US            Lititz  wWiOiD   \n",
       "24       es-es,es;q=0.8,en-us;q=0.5,en;q=0.3    ES            Bilbao  wcndER   \n",
       "25    en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4    MY      Kuala Lumpur  wcndER   \n",
       "26       ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4    CY           Nicosia  wcndER   \n",
       "27                            en-US,en;q=0.8    BR            SPaulo  zCaLwp   \n",
       "28                                     en-us  None               NaN  vNJS4H   \n",
       "29                                     en-us  None               NaN  FPX0IM   \n",
       "...                                      ...   ...               ...     ...   \n",
       "3530                          en-US,en;q=0.8    US     San Francisco  xVZg4P   \n",
       "3531                                   en-US  None               NaN  wcndER   \n",
       "3532                          en-us,en;q=0.5    US        Washington  Au3aUS   \n",
       "3533                                   en-us    US      Jacksonville  b2UtUJ   \n",
       "3534                                   en-us    US            Frisco  vNJS4H   \n",
       "3535                                   en-us    US           Houston  zIgLx8   \n",
       "3536                          en-US,en;q=0.5  None               NaN  xIcyim   \n",
       "3537     es-es,es;q=0.8,en-us;q=0.5,en;q=0.3    HN       Tegucigalpa  zCaLwp   \n",
       "3538                                   en-us    US       Los Angeles  qMac9k   \n",
       "3539                                     NaN    US          Bellevue  zu2M5o   \n",
       "3540                          en-US,en;q=0.8    US            Payson  wcndER   \n",
       "3541                                     NaN    US          Bellevue  zu2M5o   \n",
       "3542                                   en-us    US         Pittsburg  y3reI1   \n",
       "3543                                     NaN   NaN               NaN     NaN   \n",
       "3544                          en-us,en;q=0.5    US        Wentzville  vNJS4H   \n",
       "3545                          en-us,en;q=0.5    US     Saint Charles  vNJS4H   \n",
       "3546                                   en-us    US       Los Angeles  qMac9k   \n",
       "3547                                   en-us    US     Silver Spring  y0jYkg   \n",
       "3548                                   en-us    US           Mcgehee  y5rMac   \n",
       "3549     sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4    SE          Sollefte   eH8wu   \n",
       "3550                                   en-us    US      Conshohocken  A00b72   \n",
       "3551                          en-US,en;q=0.8  None               NaN  wcndER   \n",
       "3552                                     NaN    US           Decatur  rqgJuE   \n",
       "3553                                   en-us    US        Shrewsbury  9b6kNl   \n",
       "3554                                   en-us    US        Shrewsbury  axNK8c   \n",
       "3555                                      en    US           Paramus  e5SvKE   \n",
       "3556                          en-US,en;q=0.8    US     Oklahoma City  jQLtP4   \n",
       "3557                                     NaN    US             Provo  mwszkS   \n",
       "3558                                     NaN    US     Mountain View  zjtI4X   \n",
       "3559                                   en-US    US           Mc Lean  qxKrTK   \n",
       "\n",
       "       gr       h            hc           hh   kw              l  \\\n",
       "0      MA  wfLQtf  1.331823e+09    1.usa.gov  NaN        orofrog   \n",
       "1      UT  mwszkS  1.308262e+09         j.mp  NaN          bitly   \n",
       "2      DC  xxr3Qb  1.331920e+09    1.usa.gov  NaN          bitly   \n",
       "3      27  zUtuOu  1.331923e+09    1.usa.gov  NaN       alelex88   \n",
       "4      MA  9b6kNl  1.273672e+09       bit.ly  NaN          bitly   \n",
       "5      MA  axNK8c  1.273673e+09       bit.ly  NaN          bitly   \n",
       "6      77  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "7     NaN  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "8     NaN  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "9     NaN  zUtuOu  1.331923e+09    1.usa.gov  NaN       alelex88   \n",
       "10     WA  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "11     DC  A0nRz4  1.331816e+09    1.usa.gov  NaN    darrellissa   \n",
       "12     VA  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "13    NaN     NaN           NaN          NaN  NaN            NaN   \n",
       "14     GA  2rOUYc  1.255770e+09    1.usa.gov  NaN          bitly   \n",
       "15     00  rtrrth  1.317318e+09         j.mp  NaN     walkeryuen   \n",
       "16     00  qWkgbq  1.317318e+09         j.mp  NaN     walkeryuen   \n",
       "17     ME  x3jOIv  1.331840e+09    1.usa.gov  NaN  andyzieminski   \n",
       "18     UT  mwszkS  1.308262e+09    1.usa.gov  NaN          bitly   \n",
       "19     20  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "20     51  ytZYWR  1.331671e+09    bitly.com  NaN        jplnews   \n",
       "21     MD  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "22     DE  y3ZImz  1.331064e+09    1.usa.gov  NaN          bitly   \n",
       "23     PA  wWiOiD  1.330218e+09    1.usa.gov  NaN          bitly   \n",
       "24     59  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "25     14  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "26     04  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "27     27  zUtuOu  1.331923e+09    1.usa.gov  NaN       alelex88   \n",
       "28    NaN  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "29    NaN  FPX0IL  1.331923e+09    1.usa.gov  NaN   twittershare   \n",
       "...   ...     ...           ...          ...  ...            ...   \n",
       "3530   CA  wqUkTo  1.331908e+09  go.nasa.gov  NaN    nasatwitter   \n",
       "3531  NaN  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "3532   DC  A9ct6C  1.331926e+09    1.usa.gov  NaN          ncsha   \n",
       "3533   FL  ieCdgH  1.301393e+09  go.nasa.gov  NaN    nasatwitter   \n",
       "3534   TX  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3535   TX  yrPaLt  1.331903e+09      aash.to  NaN         aashto   \n",
       "3536  NaN  yG1TTf  1.331728e+09  go.nasa.gov  NaN    nasatwitter   \n",
       "3537   08  w63FZW  1.331547e+09    1.usa.gov  NaN      bufferapp   \n",
       "3538   CA  qds1Ge  1.310474e+09    1.usa.gov  NaN  healthypeople   \n",
       "3539   WA  zDhdro  1.331586e+09       bit.ly  NaN       glimtwin   \n",
       "3540   UT  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "3541   WA  zDhdro  1.331586e+09    1.usa.gov  NaN       glimtwin   \n",
       "3542   CA  y3reI1  1.331926e+09    1.usa.gov  NaN          bitly   \n",
       "3543  NaN     NaN           NaN          NaN  NaN            NaN   \n",
       "3544   MO  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3545   IL  u0uD9q  1.319564e+09    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3546   CA  qds1Ge  1.310474e+09    1.usa.gov  NaN  healthypeople   \n",
       "3547   MD  y0jYkg  1.331852e+09    1.usa.gov  NaN          bitly   \n",
       "3548   AR  xANY6O  1.331916e+09    1.usa.gov  NaN    twitterfeed   \n",
       "3549   24  7dtjei  1.260316e+09    1.usa.gov  NaN   tweetdeckapi   \n",
       "3550   PA  yGSwzn  1.331918e+09    1.usa.gov  NaN        addthis   \n",
       "3551  NaN  zkpJBR  1.331923e+09    1.usa.gov  NaN       bnjacobs   \n",
       "3552   AL  xcz8vt  1.331227e+09    1.usa.gov  NaN      bootsnall   \n",
       "3553   MA  9b6kNl  1.273672e+09       bit.ly  NaN          bitly   \n",
       "3554   MA  axNK8c  1.273673e+09       bit.ly  NaN          bitly   \n",
       "3555   NJ  fqPSr9  1.301298e+09    1.usa.gov  NaN   tweetdeckapi   \n",
       "3556   OK  jQLtP4  1.307530e+09    1.usa.gov  NaN          bitly   \n",
       "3557   UT  mwszkS  1.308262e+09         j.mp  NaN          bitly   \n",
       "3558   CA  zjtI4X  1.327529e+09    1.usa.gov  NaN          bitly   \n",
       "3559   VA  qxKrTK  1.312898e+09    1.usa.gov  NaN          bitly   \n",
       "\n",
       "                            ll   nk  \\\n",
       "0      [42.576698, -70.954903]  1.0   \n",
       "1     [40.218102, -111.613297]  0.0   \n",
       "2        [38.9007, -77.043098]  1.0   \n",
       "3     [-23.549999, -46.616699]  0.0   \n",
       "4      [42.286499, -71.714699]  0.0   \n",
       "5      [42.286499, -71.714699]  0.0   \n",
       "6         [51.116699, 15.2833]  0.0   \n",
       "7                          NaN  0.0   \n",
       "8                          NaN  0.0   \n",
       "9                          NaN  0.0   \n",
       "10      [47.5951, -122.332603]  1.0   \n",
       "11     [38.937599, -77.092796]  0.0   \n",
       "12     [38.790901, -77.094704]  1.0   \n",
       "13                         NaN  NaN   \n",
       "14       [33.953201, -84.5177]  1.0   \n",
       "15       [22.2833, 114.150002]  1.0   \n",
       "16       [22.2833, 114.150002]  1.0   \n",
       "17     [44.299702, -70.369797]  0.0   \n",
       "18    [40.218102, -111.613297]  0.0   \n",
       "19        [45.438599, 12.3267]  0.0   \n",
       "20        [37.516701, -5.9833]  0.0   \n",
       "21     [38.939201, -76.635002]  0.0   \n",
       "22        [39.785, -75.682297]  0.0   \n",
       "23       [40.174999, -76.3078]  0.0   \n",
       "24            [43.25, -2.9667]  0.0   \n",
       "25        [3.1667, 101.699997]  0.0   \n",
       "26      [35.166698, 33.366699]  0.0   \n",
       "27      [-23.5333, -46.616699]  0.0   \n",
       "28                         NaN  0.0   \n",
       "29                         NaN  1.0   \n",
       "...                        ...  ...   \n",
       "3530    [37.7645, -122.429398]  0.0   \n",
       "3531                       NaN  0.0   \n",
       "3532   [38.904202, -77.031998]  1.0   \n",
       "3533   [30.279301, -81.585098]  1.0   \n",
       "3534   [33.149899, -96.855499]  1.0   \n",
       "3535   [29.775499, -95.415199]  1.0   \n",
       "3536                       NaN  0.0   \n",
       "3537        [14.1, -87.216698]  0.0   \n",
       "3538  [34.041599, -118.298798]  0.0   \n",
       "3539  [47.615398, -122.210297]  0.0   \n",
       "3540  [40.014198, -111.738899]  0.0   \n",
       "3541  [47.615398, -122.210297]  0.0   \n",
       "3542    [38.0051, -121.838699]  0.0   \n",
       "3543                       NaN  NaN   \n",
       "3544   [38.790001, -90.854897]  1.0   \n",
       "3545     [41.9352, -88.290901]  1.0   \n",
       "3546  [34.041599, -118.298798]  1.0   \n",
       "3547   [39.052101, -77.014999]  1.0   \n",
       "3548   [33.628399, -91.356903]  1.0   \n",
       "3549    [63.166698, 17.266701]  1.0   \n",
       "3550       [40.0798, -75.2855]  0.0   \n",
       "3551                       NaN  0.0   \n",
       "3552   [34.572701, -86.940598]  0.0   \n",
       "3553   [42.286499, -71.714699]  0.0   \n",
       "3554   [42.286499, -71.714699]  0.0   \n",
       "3555         [40.9445, -74.07]  1.0   \n",
       "3556     [35.4715, -97.518997]  0.0   \n",
       "3557  [40.218102, -111.613297]  0.0   \n",
       "3558  [37.419201, -122.057404]  0.0   \n",
       "3559   [38.935799, -77.162102]  0.0   \n",
       "\n",
       "                                                      r             t  \\\n",
       "0     http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1.331923e+09   \n",
       "1                              http://www.AwareMap.com/  1.331923e+09   \n",
       "2                                  http://t.co/03elZC4Q  1.331923e+09   \n",
       "3                                                direct  1.331923e+09   \n",
       "4                   http://www.shrewsbury-ma.gov/selco/  1.331923e+09   \n",
       "5                   http://www.shrewsbury-ma.gov/selco/  1.331923e+09   \n",
       "6     http://plus.url.google.com/url?sa=z&n=13319232...  1.331923e+09   \n",
       "7                              http://www.facebook.com/  1.331923e+09   \n",
       "8     http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1.331923e+09   \n",
       "9                                  http://t.co/o1Pd0WeV  1.331923e+09   \n",
       "10                                               direct  1.331923e+09   \n",
       "11                                 http://t.co/ND7SoPyo  1.331923e+09   \n",
       "12                                               direct  1.331923e+09   \n",
       "13                                                  NaN           NaN   \n",
       "14                                               direct  1.331923e+09   \n",
       "15    http://forum2.hkgolden.com/view.aspx?type=BW&m...  1.331923e+09   \n",
       "16    http://forum2.hkgolden.com/view.aspx?type=BW&m...  1.331923e+09   \n",
       "17                                 http://t.co/6Cx4ROLs  1.331923e+09   \n",
       "18                             http://www.AwareMap.com/  1.331923e+09   \n",
       "19                             http://www.facebook.com/  1.331923e+09   \n",
       "20                             http://www.facebook.com/  1.331923e+09   \n",
       "21                             http://www.facebook.com/  1.331923e+09   \n",
       "22                                               direct  1.331923e+09   \n",
       "23    http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1.331923e+09   \n",
       "24                             http://www.facebook.com/  1.331923e+09   \n",
       "25                             http://www.facebook.com/  1.331923e+09   \n",
       "26                 http://www.facebook.com/?ref=tn_tnmn  1.331923e+09   \n",
       "27                                               direct  1.331923e+09   \n",
       "28                                               direct  1.331923e+09   \n",
       "29                                 http://t.co/5xlp0B34  1.331923e+09   \n",
       "...                                                 ...           ...   \n",
       "3530  http://www.facebook.com/l.php?u=http%3A%2F%2Fg...  1.331927e+09   \n",
       "3531                                             direct  1.331927e+09   \n",
       "3532                              http://www.ncsha.org/  1.331927e+09   \n",
       "3533                                             direct  1.331927e+09   \n",
       "3534                                             direct  1.331927e+09   \n",
       "3535                                             direct  1.331927e+09   \n",
       "3536                               http://t.co/g1VKE8zS  1.331927e+09   \n",
       "3537                               http://t.co/A8TJyibE  1.331927e+09   \n",
       "3538                                             direct  1.331927e+09   \n",
       "3539                                             direct  1.331927e+09   \n",
       "3540  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1.331927e+09   \n",
       "3541                                             direct  1.331927e+09   \n",
       "3542  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1.331927e+09   \n",
       "3543                                                NaN           NaN   \n",
       "3544                                             direct  1.331927e+09   \n",
       "3545                                             direct  1.331927e+09   \n",
       "3546                                             direct  1.331927e+09   \n",
       "3547                                             direct  1.331927e+09   \n",
       "3548  https://twitter.com/fdarecalls/status/18069759...  1.331927e+09   \n",
       "3549                                             direct  1.331927e+09   \n",
       "3550   http://www.linkedin.com/home?trk=hb_tab_home_top  1.331927e+09   \n",
       "3551  http://plus.url.google.com/url?sa=z&n=13319268...  1.331927e+09   \n",
       "3552                                             direct  1.331927e+09   \n",
       "3553                http://www.shrewsbury-ma.gov/selco/  1.331927e+09   \n",
       "3554                http://www.shrewsbury-ma.gov/selco/  1.331927e+09   \n",
       "3555                                             direct  1.331927e+09   \n",
       "3556  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1.331927e+09   \n",
       "3557                           http://www.AwareMap.com/  1.331927e+09   \n",
       "3558                                             direct  1.331927e+09   \n",
       "3559                               http://t.co/OEEEvwjU  1.331927e+09   \n",
       "\n",
       "                       tz                                                  u  \n",
       "0        America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "1          America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "2        America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
       "3       America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "4        America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "5        America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "6           Europe/Warsaw  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "7                          http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "8                          http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "9                                    http://apod.nasa.gov/apod/ap120312.html  \n",
       "10    America/Los_Angeles  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "11       America/New_York  http://oversight.house.gov/wp-content/uploads/...  \n",
       "12       America/New_York  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "13                    NaN                                                NaN  \n",
       "14       America/New_York               http://toxtown.nlm.nih.gov/index.php  \n",
       "15         Asia/Hong_Kong  http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr...  \n",
       "16         Asia/Hong_Kong  http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc...  \n",
       "17       America/New_York  http://www.usda.gov/wps/portal/usda/usdahome?c...  \n",
       "18         America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "19            Europe/Rome  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "20           Africa/Ceuta  http://voyager.jpl.nasa.gov/imagesvideo/uranus...  \n",
       "21       America/New_York  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "22       America/New_York  http://portal.hud.gov/hudportal/documents/hudd...  \n",
       "23       America/New_York  http://www.tricare.mil/mybenefit/ProfileFilter...  \n",
       "24          Europe/Madrid  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "25      Asia/Kuala_Lumpur  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "26           Asia/Nicosia  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "27      America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "28                         https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "29                         http://www.ed.gov/news/media-advisories/us-dep...  \n",
       "...                   ...                                                ...  \n",
       "3530  America/Los_Angeles  http://www.nasa.gov/multimedia/imagegallery/im...  \n",
       "3531                       http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3532     America/New_York  http://portal.hud.gov/hudportal/HUD?src=/press...  \n",
       "3533     America/New_York                         http://apod.nasa.gov/apod/  \n",
       "3534      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3535      America/Chicago  http://ntl.bts.gov/lib/44000/44300/44374/FHWA-...  \n",
       "3536                       http://www.nasa.gov/mission_pages/hurricanes/a...  \n",
       "3537  America/Tegucigalpa            http://apod.nasa.gov/apod/ap120312.html  \n",
       "3538  America/Los_Angeles  http://healthypeople.gov/2020/connect/webinars...  \n",
       "3539  America/Los_Angeles  http://www.federalreserve.gov/newsevents/press...  \n",
       "3540       America/Denver  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3541  America/Los_Angeles  http://www.federalreserve.gov/newsevents/press...  \n",
       "3542  America/Los_Angeles  http://www.sba.gov/community/blogs/community-b...  \n",
       "3543                  NaN                                                NaN  \n",
       "3544      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3545      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3546  America/Los_Angeles  http://healthypeople.gov/2020/connect/webinars...  \n",
       "3547     America/New_York  http://www.epa.gov/otaq/regs/fuels/additive/e1...  \n",
       "3548      America/Chicago    http://www.fda.gov/Safety/Recalls/ucm296326.htm  \n",
       "3549     Europe/Stockholm  http://www.nasa.gov/mission_pages/WISE/main/in...  \n",
       "3550     America/New_York  http://www.nlm.nih.gov/medlineplus/news/fullst...  \n",
       "3551                       http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3552      America/Chicago  http://travel.state.gov/passport/passport_5535...  \n",
       "3553     America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "3554     America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "3555     America/New_York  http://www.fda.gov/AdvisoryCommittees/Committe...  \n",
       "3556      America/Chicago  http://www.okc.gov/PublicNotificationSystem/Fo...  \n",
       "3557       America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "3558  America/Los_Angeles                http://www.ahrq.gov/qual/qitoolkit/  \n",
       "3559     America/New_York  http://herndon-va.gov/Content/public_safety/Pu...  \n",
       "\n",
       "[3560 rows x 18 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame, Series\n",
    "import pandas as pd\n",
    "\n",
    "frame = DataFrame(records)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.309735Z",
     "start_time": "2019-01-19T00:48:07.276588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "5     America/New_York\n",
       "6        Europe/Warsaw\n",
       "7                     \n",
       "8                     \n",
       "9                     \n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.343814Z",
     "start_time": "2019-01-19T00:48:07.312245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "America/Sao_Paulo        33\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.387201Z",
     "start_time": "2019-01-19T00:48:07.346150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.423314Z",
     "start_time": "2019-01-19T00:48:07.389945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f09cc90>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f09cc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.759237Z",
     "start_time": "2019-01-19T00:48:07.426116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1157347d0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VjsCP230BzgHeVVW7DBpupLp3bTXOB45MsvUY5kSSJGlKmYrbCS6pqsfa8T7A3wNU1Z1J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\n/w8Q/q3DVgApgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233aa5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_counts[:10].plot(kind='barh', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.811643Z",
     "start_time": "2019-01-19T00:48:07.761907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _heartbeat_                                                  a  \\\n",
       "0          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1          NaN                             GoogleMaps/RochesterNY   \n",
       "2          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "\n",
       "               al   c          cy       g  gr       h            hc  \\\n",
       "0  en-US,en;q=0.8  US     Danvers  A6qOVH  MA  wfLQtf  1.331823e+09   \n",
       "1             NaN  US       Provo  mwszkS  UT  mwszkS  1.308262e+09   \n",
       "2           en-US  US  Washington  xxr3Qb  DC  xxr3Qb  1.331920e+09   \n",
       "3           pt-br  BR        Braz  zCaLwp  27  zUtuOu  1.331923e+09   \n",
       "4  en-US,en;q=0.8  US  Shrewsbury  9b6kNl  MA  9b6kNl  1.273672e+09   \n",
       "\n",
       "          hh   kw         l                        ll   nk  \\\n",
       "0  1.usa.gov  NaN   orofrog   [42.576698, -70.954903]  1.0   \n",
       "1       j.mp  NaN     bitly  [40.218102, -111.613297]  0.0   \n",
       "2  1.usa.gov  NaN     bitly     [38.9007, -77.043098]  1.0   \n",
       "3  1.usa.gov  NaN  alelex88  [-23.549999, -46.616699]  0.0   \n",
       "4     bit.ly  NaN     bitly   [42.286499, -71.714699]  0.0   \n",
       "\n",
       "                                                   r             t  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1.331923e+09   \n",
       "1                           http://www.AwareMap.com/  1.331923e+09   \n",
       "2                               http://t.co/03elZC4Q  1.331923e+09   \n",
       "3                                             direct  1.331923e+09   \n",
       "4                http://www.shrewsbury-ma.gov/selco/  1.331923e+09   \n",
       "\n",
       "                  tz                                                  u  \n",
       "0   America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "1     America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "2   America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
       "3  America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "4   America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.844052Z",
     "start_time": "2019-01-19T00:48:07.814234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.892325Z",
     "start_time": "2019-01-19T00:48:07.846412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.932188Z",
     "start_time": "2019-01-19T00:48:07.895236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:07.974228Z",
     "start_time": "2019-01-19T00:48:07.934692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.050462Z",
     "start_time": "2019-01-19T00:48:07.976918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.098648Z",
     "start_time": "2019-01-19T00:48:08.058050Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.137554Z",
     "start_time": "2019-01-19T00:48:08.101158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'],\n",
       "      dtype='|S11')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operating_system = np.where(cframe['a'].str.contains('Windows'),\n",
    "                            'Windows', 'Not Windows')\n",
    "operating_system[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.201400Z",
     "start_time": "2019-01-19T00:48:08.150448Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', operating_system])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.246839Z",
     "start_time": "2019-01-19T00:48:08.205697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.296330Z",
     "start_time": "2019-01-19T00:48:08.249696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use to sort in ascending order\n",
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.364794Z",
     "start_time": "2019-01-19T00:48:08.305819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.400757Z",
     "start_time": "2019-01-19T00:48:08.367788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11219c6d0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11219c6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.812991Z",
     "start_time": "2019-01-19T00:48:08.403115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12409f590>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgZ0AkmwD3FlVy1dy3euBA5rjQ4Gr285dCvw7cEmzH5mquh94BDgOOLvp9zNg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\n/EqSJKkZNr+SJElqxh+W2HdytTtsrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122fd2890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:08.858696Z",
     "start_time": "2019-01-19T00:48:08.816355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d701d0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d701d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T00:48:09.267346Z",
     "start_time": "2019-01-19T00:48:08.862216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12efe9790>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUcB1Sa5oxtkRGF9V9/Tr3ZUkSRpGhkP4PZJOiAU4v3l9KXBzVT0AkOQ3wOLw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\n8rL6DdeZ39Wiqr452DVIkiS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11219cb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_subset = count_subset.div(count_subset.sum(1), axis=0)\n",
    "normed_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MovieLens 1M data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:58.789495Z",
     "start_time": "2019-01-19T02:00:51.311156Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas_young/miniconda3/envs/bunnies/lib/python2.7/site-packages/ipykernel_launcher.py:13: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  del sys.path[0]\n",
      "/Users/thomas_young/miniconda3/envs/bunnies/lib/python2.7/site-packages/ipykernel_launcher.py:14: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n",
      "/Users/thomas_young/miniconda3/envs/bunnies/lib/python2.7/site-packages/ipykernel_launcher.py:15: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "encoding = 'latin1'\n",
    "\n",
    "upath = os.path.expanduser('ch02/movielens/users.dat')\n",
    "rpath = os.path.expanduser('ch02/movielens/ratings.dat')\n",
    "mpath = os.path.expanduser('ch02/movielens/movies.dat')\n",
    "\n",
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "\n",
    "users = pd.read_csv(upath, sep='::', header=None, names=unames, encoding=encoding)\n",
    "ratings = pd.read_csv(rpath, sep='::', header=None, names=rnames, encoding=encoding)\n",
    "movies = pd.read_csv(mpath, sep='::', header=None, names=mnames, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:59.169863Z",
     "start_time": "2019-01-19T02:00:58.791867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1::F::1::10::48067\r\n",
      "2::M::56::16::70072\r\n",
      "3::M::25::15::55117\r\n",
      "4::M::45::7::02460\r\n",
      "5::M::25::20::55455\r\n"
     ]
    }
   ],
   "source": [
    "!head -5 $upath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:59.223955Z",
     "start_time": "2019-01-19T02:00:59.173957Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
       "3        4      M   45           7  02460\n",
       "4        5      M   25          20  55455"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:59.264820Z",
     "start_time": "2019-01-19T02:00:59.226624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:59.315470Z",
     "start_time": "2019-01-19T02:00:59.269529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:00:59.355488Z",
     "start_time": "2019-01-19T02:00:59.318217Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
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       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:18:43.829432Z",
     "start_time": "2019-01-19T02:18:42.874093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
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       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>4714</td>\n",
       "      <td>2804</td>\n",
       "      <td>3</td>\n",
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       "      <td>50</td>\n",
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       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>4715</td>\n",
       "      <td>2804</td>\n",
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       "      <td>963444852</td>\n",
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       "      <td>Christmas Story, A (1983)</td>\n",
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       "      <td>Christmas Story, A (1983)</td>\n",
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       "      <td>2804</td>\n",
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       "      <td>963270515</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>32580</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10004</th>\n",
       "      <td>4742</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>963330019</td>\n",
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       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>94118</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "10000     4714      2804       3  963446973      M   50           1  55405   \n",
       "10001     4715      2804       5  963444852      M   25           2  97205   \n",
       "10002     4732      2804       5  963407540      M   25          14  24450   \n",
       "10003     4739      2804       5  963270515      M   25           3  32580   \n",
       "10004     4742      2804       5  963330019      M   25           3  94118   \n",
       "\n",
       "                           title        genres  \n",
       "10000  Christmas Story, A (1983)  Comedy|Drama  \n",
       "10001  Christmas Story, A (1983)  Comedy|Drama  \n",
       "10002  Christmas Story, A (1983)  Comedy|Drama  \n",
       "10003  Christmas Story, A (1983)  Comedy|Drama  \n",
       "10004  Christmas Story, A (1983)  Comedy|Drama  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data[10000:10005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:22:53.247398Z",
     "start_time": "2019-01-19T02:18:46.163537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mis�rables, Les (1995)\n",
      "Misrables, Les (1995)\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "print(data.title.loc[372300])\n",
    "# 解决编码问题\n",
    "title = data.title\n",
    "# https://stackoverflow.com/questions/20078816/replace-non-ascii-characters-with-a-single-space/39059279\n",
    "for i, row in enumerate(title):\n",
    "    try:\n",
    "        row.decode('ascii')\n",
    "    except UnicodeDecodeError:\n",
    "        row = re.sub(r'[^\\x00-\\x7F]+','', row)\n",
    "#         row = \"\".join(i for i in row if ord(i)<128)\n",
    "        title.iloc[i] = row\n",
    "data.title = title\n",
    "print(data.title.loc[372300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:01.211326Z",
     "start_time": "2019-01-19T02:23:00.870926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                F         M\n",
       "title                                            \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905\n",
       "'Night Mother (1986)           3.388889  3.352941\n",
       "'Til There Was You (1997)      2.675676  2.733333\n",
       "'burbs, The (1989)             2.793478  2.962085\n",
       "...And Justice for All (1979)  3.828571  3.689024"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                                columns='gender', aggfunc='mean')\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:02.218753Z",
     "start_time": "2019-01-19T02:23:02.109418Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratings_by_title = data.groupby('title').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:03.715928Z",
     "start_time": "2019-01-19T02:23:03.667812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "$1,000,000 Duck (1971)            37\n",
       "'Night Mother (1986)              70\n",
       "'Til There Was You (1997)         52\n",
       "'burbs, The (1989)               303\n",
       "...And Justice for All (1979)    199\n",
       "dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:05.102596Z",
     "start_time": "2019-01-19T02:23:05.076403Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:05.620469Z",
     "start_time": "2019-01-19T02:23:05.591961Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u''burbs, The (1989)', u'10 Things I Hate About You (1999)',\n",
       "       u'101 Dalmatians (1961)', u'101 Dalmatians (1996)',\n",
       "       u'12 Angry Men (1957)', u'13th Warrior, The (1999)',\n",
       "       u'2 Days in the Valley (1996)', u'20,000 Leagues Under the Sea (1954)',\n",
       "       u'2001: A Space Odyssey (1968)', u'2010 (1984)'],\n",
       "      dtype='object', name=u'title')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:07.060996Z",
     "start_time": "2019-01-19T02:23:07.022658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.646552</td>\n",
       "      <td>3.311966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13th Warrior, The (1999)</th>\n",
       "      <td>3.112000</td>\n",
       "      <td>3.168000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td>3.488889</td>\n",
       "      <td>3.244813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <td>3.670103</td>\n",
       "      <td>3.709205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <td>3.825581</td>\n",
       "      <td>4.129738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 (1984)</th>\n",
       "      <td>3.446809</td>\n",
       "      <td>3.413712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                      F         M\n",
       "title                                                  \n",
       "'burbs, The (1989)                   2.793478  2.962085\n",
       "10 Things I Hate About You (1999)    3.646552  3.311966\n",
       "101 Dalmatians (1961)                3.791444  3.500000\n",
       "101 Dalmatians (1996)                3.240000  2.911215\n",
       "12 Angry Men (1957)                  4.184397  4.328421\n",
       "13th Warrior, The (1999)             3.112000  3.168000\n",
       "2 Days in the Valley (1996)          3.488889  3.244813\n",
       "20,000 Leagues Under the Sea (1954)  3.670103  3.709205\n",
       "2001: A Space Odyssey (1968)         3.825581  4.129738\n",
       "2010 (1984)                          3.446809  3.413712"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = mean_ratings.loc[active_titles]\n",
    "mean_ratings[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:09.132722Z",
     "start_time": "2019-01-19T02:23:09.105602Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_ratings = mean_ratings.rename(index={'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n",
    "                           'Seven Samurai (Shichinin no samurai) (1954)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:11.799183Z",
     "start_time": "2019-01-19T02:23:11.763129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>4.644444</td>\n",
       "      <td>4.473795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>4.588235</td>\n",
       "      <td>4.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>4.572650</td>\n",
       "      <td>4.464589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
       "      <td>4.563107</td>\n",
       "      <td>4.385075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.562602</td>\n",
       "      <td>4.491415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.539075</td>\n",
       "      <td>4.560625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Day Out, A (1992)</th>\n",
       "      <td>4.537879</td>\n",
       "      <td>4.293255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>4.536667</td>\n",
       "      <td>4.372611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creature Comforts (1990)</th>\n",
       "      <td>4.513889</td>\n",
       "      <td>4.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.513317</td>\n",
       "      <td>4.518248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "Close Shave, A (1995)                               4.644444  4.473795\n",
       "Wrong Trousers, The (1993)                          4.588235  4.478261\n",
       "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)       4.572650  4.464589\n",
       "Wallace & Gromit: The Best of Aardman Animation...  4.563107  4.385075\n",
       "Schindler's List (1993)                             4.562602  4.491415\n",
       "Shawshank Redemption, The (1994)                    4.539075  4.560625\n",
       "Grand Day Out, A (1992)                             4.537879  4.293255\n",
       "To Kill a Mockingbird (1962)                        4.536667  4.372611\n",
       "Creature Comforts (1990)                            4.513889  4.272277\n",
       "Usual Suspects, The (1995)                          4.513317  4.518248"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring rating disagreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:12.428331Z",
     "start_time": "2019-01-19T02:23:12.401959Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:13.481251Z",
     "start_time": "2019-01-19T02:23:13.417143Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dirty Dancing (1987)</th>\n",
       "      <td>3.790378</td>\n",
       "      <td>2.959596</td>\n",
       "      <td>-0.830782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumpin' Jack Flash (1986)</th>\n",
       "      <td>3.254717</td>\n",
       "      <td>2.578358</td>\n",
       "      <td>-0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grease (1978)</th>\n",
       "      <td>3.975265</td>\n",
       "      <td>3.367041</td>\n",
       "      <td>-0.608224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Women (1994)</th>\n",
       "      <td>3.870588</td>\n",
       "      <td>3.321739</td>\n",
       "      <td>-0.548849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel Magnolias (1989)</th>\n",
       "      <td>3.901734</td>\n",
       "      <td>3.365957</td>\n",
       "      <td>-0.535777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anastasia (1997)</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.281609</td>\n",
       "      <td>-0.518391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
       "      <td>3.673016</td>\n",
       "      <td>3.160131</td>\n",
       "      <td>-0.512885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color Purple, The (1985)</th>\n",
       "      <td>4.158192</td>\n",
       "      <td>3.659341</td>\n",
       "      <td>-0.498851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Innocence, The (1993)</th>\n",
       "      <td>3.827068</td>\n",
       "      <td>3.339506</td>\n",
       "      <td>-0.487561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Willy (1993)</th>\n",
       "      <td>2.921348</td>\n",
       "      <td>2.438776</td>\n",
       "      <td>-0.482573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Kiss (1995)</th>\n",
       "      <td>3.535714</td>\n",
       "      <td>3.056962</td>\n",
       "      <td>-0.478752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Shop of Horrors, The (1960)</th>\n",
       "      <td>3.650000</td>\n",
       "      <td>3.179688</td>\n",
       "      <td>-0.470312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guys and Dolls (1955)</th>\n",
       "      <td>4.051724</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>-0.468391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary Poppins (1964)</th>\n",
       "      <td>4.197740</td>\n",
       "      <td>3.730594</td>\n",
       "      <td>-0.467147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patch Adams (1998)</th>\n",
       "      <td>3.473282</td>\n",
       "      <td>3.008746</td>\n",
       "      <td>-0.464536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                        F         M      diff\n",
       "title                                                              \n",
       "Dirty Dancing (1987)                   3.790378  2.959596 -0.830782\n",
       "Jumpin' Jack Flash (1986)              3.254717  2.578358 -0.676359\n",
       "Grease (1978)                          3.975265  3.367041 -0.608224\n",
       "Little Women (1994)                    3.870588  3.321739 -0.548849\n",
       "Steel Magnolias (1989)                 3.901734  3.365957 -0.535777\n",
       "Anastasia (1997)                       3.800000  3.281609 -0.518391\n",
       "Rocky Horror Picture Show, The (1975)  3.673016  3.160131 -0.512885\n",
       "Color Purple, The (1985)               4.158192  3.659341 -0.498851\n",
       "Age of Innocence, The (1993)           3.827068  3.339506 -0.487561\n",
       "Free Willy (1993)                      2.921348  2.438776 -0.482573\n",
       "French Kiss (1995)                     3.535714  3.056962 -0.478752\n",
       "Little Shop of Horrors, The (1960)     3.650000  3.179688 -0.470312\n",
       "Guys and Dolls (1955)                  4.051724  3.583333 -0.468391\n",
       "Mary Poppins (1964)                    4.197740  3.730594 -0.467147\n",
       "Patch Adams (1998)                     3.473282  3.008746 -0.464536"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff = mean_ratings.sort_values(by='diff')\n",
    "sorted_by_diff[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:13.849799Z",
     "start_time": "2019-01-19T02:23:13.800158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Good, The Bad and The Ugly, The (1966)</th>\n",
       "      <td>3.494949</td>\n",
       "      <td>4.221300</td>\n",
       "      <td>0.726351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky Fried Movie, The (1977)</th>\n",
       "      <td>2.878788</td>\n",
       "      <td>3.555147</td>\n",
       "      <td>0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dumb &amp; Dumber (1994)</th>\n",
       "      <td>2.697987</td>\n",
       "      <td>3.336595</td>\n",
       "      <td>0.638608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest Day, The (1962)</th>\n",
       "      <td>3.411765</td>\n",
       "      <td>4.031447</td>\n",
       "      <td>0.619682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cable Guy, The (1996)</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.863787</td>\n",
       "      <td>0.613787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
       "      <td>3.297297</td>\n",
       "      <td>3.909283</td>\n",
       "      <td>0.611985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hidden, The (1987)</th>\n",
       "      <td>3.137931</td>\n",
       "      <td>3.745098</td>\n",
       "      <td>0.607167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky III (1982)</th>\n",
       "      <td>2.361702</td>\n",
       "      <td>2.943503</td>\n",
       "      <td>0.581801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caddyshack (1980)</th>\n",
       "      <td>3.396135</td>\n",
       "      <td>3.969737</td>\n",
       "      <td>0.573602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>For a Few Dollars More (1965)</th>\n",
       "      <td>3.409091</td>\n",
       "      <td>3.953795</td>\n",
       "      <td>0.544704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porky's (1981)</th>\n",
       "      <td>2.296875</td>\n",
       "      <td>2.836364</td>\n",
       "      <td>0.539489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal House (1978)</th>\n",
       "      <td>3.628906</td>\n",
       "      <td>4.167192</td>\n",
       "      <td>0.538286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exorcist, The (1973)</th>\n",
       "      <td>3.537634</td>\n",
       "      <td>4.067239</td>\n",
       "      <td>0.529605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fright Night (1985)</th>\n",
       "      <td>2.973684</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barb Wire (1996)</th>\n",
       "      <td>1.585366</td>\n",
       "      <td>2.100386</td>\n",
       "      <td>0.515020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351\n",
       "Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359\n",
       "Dumb & Dumber (1994)                    2.697987  3.336595  0.638608\n",
       "Longest Day, The (1962)                 3.411765  4.031447  0.619682\n",
       "Cable Guy, The (1996)                   2.250000  2.863787  0.613787\n",
       "Evil Dead II (Dead By Dawn) (1987)      3.297297  3.909283  0.611985\n",
       "Hidden, The (1987)                      3.137931  3.745098  0.607167\n",
       "Rocky III (1982)                        2.361702  2.943503  0.581801\n",
       "Caddyshack (1980)                       3.396135  3.969737  0.573602\n",
       "For a Few Dollars More (1965)           3.409091  3.953795  0.544704\n",
       "Porky's (1981)                          2.296875  2.836364  0.539489\n",
       "Animal House (1978)                     3.628906  4.167192  0.538286\n",
       "Exorcist, The (1973)                    3.537634  4.067239  0.529605\n",
       "Fright Night (1985)                     2.973684  3.500000  0.526316\n",
       "Barb Wire (1996)                        1.585366  2.100386  0.515020"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse order of rows, take first 15 rows\n",
    "sorted_by_diff[::-1][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:14.618382Z",
     "start_time": "2019-01-19T02:23:14.486065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Dumb & Dumber (1994)                     1.321333\n",
       "Blair Witch Project, The (1999)          1.316368\n",
       "Natural Born Killers (1994)              1.307198\n",
       "Tank Girl (1995)                         1.277695\n",
       "Rocky Horror Picture Show, The (1975)    1.260177\n",
       "Eyes Wide Shut (1999)                    1.259624\n",
       "Evita (1996)                             1.253631\n",
       "Billy Madison (1995)                     1.249970\n",
       "Fear and Loathing in Las Vegas (1998)    1.246408\n",
       "Bicentennial Man (1999)                  1.245533\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard deviation of rating grouped by title\n",
    "rating_std_by_title = data.groupby('title')['rating'].std()\n",
    "# Filter down to active_titles\n",
    "rating_std_by_title = rating_std_by_title.loc[active_titles]\n",
    "# Order Series by value in descending order\n",
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:18.194655Z",
     "start_time": "2019-01-19T02:23:18.096154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.DataFrameGroupBy object at 0x123fc0290>\n",
      "<pandas.core.groupby.SeriesGroupBy object at 0x13570ea50>\n"
     ]
    }
   ],
   "source": [
    "rating_std_by_title = data.groupby('title')\n",
    "print(rating_std_by_title)\n",
    "print(rating_std_by_title['rating'])\n",
    "rating_std_by_title = data.groupby('title')['rating'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US Baby Names 1880-2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:24.662024Z",
     "start_time": "2019-01-19T02:23:24.609298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/Users/thomas_young/Documents/git_download/pydata-book'"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(12, 5))\n",
    "np.set_printoptions(precision=4)\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://www.ssa.gov/oact/babynames/limits.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:23:28.353610Z",
     "start_time": "2019-01-19T02:23:28.187858Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 10 ch02/names/yob1880.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:30:51.666388Z",
     "start_time": "2019-01-19T02:30:51.619671Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births\n",
       "0       Mary   F    7065\n",
       "1       Anna   F    2604\n",
       "2       Emma   F    2003\n",
       "3  Elizabeth   F    1939\n",
       "4     Minnie   F    1746"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names1880 = pd.read_csv('ch02/names/yob1880.txt', names=['name', 'sex', 'births'])\n",
    "names1880.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:30:54.074535Z",
     "start_time": "2019-01-19T02:30:54.044118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:30:55.025801Z",
     "start_time": "2019-01-19T02:30:54.848092Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NationalReadMe.pdf yob1912.txt        yob1945.txt        yob1978.txt\r\n",
      "yob1880.txt        yob1913.txt        yob1946.txt        yob1979.txt\r\n",
      "yob1881.txt        yob1914.txt        yob1947.txt        yob1980.txt\r\n",
      "yob1882.txt        yob1915.txt        yob1948.txt        yob1981.txt\r\n",
      "yob1883.txt        yob1916.txt        yob1949.txt        yob1982.txt\r\n",
      "yob1884.txt        yob1917.txt        yob1950.txt        yob1983.txt\r\n",
      "yob1885.txt        yob1918.txt        yob1951.txt        yob1984.txt\r\n",
      "yob1886.txt        yob1919.txt        yob1952.txt        yob1985.txt\r\n",
      "yob1887.txt        yob1920.txt        yob1953.txt        yob1986.txt\r\n",
      "yob1888.txt        yob1921.txt        yob1954.txt        yob1987.txt\r\n",
      "yob1889.txt        yob1922.txt        yob1955.txt        yob1988.txt\r\n",
      "yob1890.txt        yob1923.txt        yob1956.txt        yob1989.txt\r\n",
      "yob1891.txt        yob1924.txt        yob1957.txt        yob1990.txt\r\n",
      "yob1892.txt        yob1925.txt        yob1958.txt        yob1991.txt\r\n",
      "yob1893.txt        yob1926.txt        yob1959.txt        yob1992.txt\r\n",
      "yob1894.txt        yob1927.txt        yob1960.txt        yob1993.txt\r\n",
      "yob1895.txt        yob1928.txt        yob1961.txt        yob1994.txt\r\n",
      "yob1896.txt        yob1929.txt        yob1962.txt        yob1995.txt\r\n",
      "yob1897.txt        yob1930.txt        yob1963.txt        yob1996.txt\r\n",
      "yob1898.txt        yob1931.txt        yob1964.txt        yob1997.txt\r\n",
      "yob1899.txt        yob1932.txt        yob1965.txt        yob1998.txt\r\n",
      "yob1900.txt        yob1933.txt        yob1966.txt        yob1999.txt\r\n",
      "yob1901.txt        yob1934.txt        yob1967.txt        yob2000.txt\r\n",
      "yob1902.txt        yob1935.txt        yob1968.txt        yob2001.txt\r\n",
      "yob1903.txt        yob1936.txt        yob1969.txt        yob2002.txt\r\n",
      "yob1904.txt        yob1937.txt        yob1970.txt        yob2003.txt\r\n",
      "yob1905.txt        yob1938.txt        yob1971.txt        yob2004.txt\r\n",
      "yob1906.txt        yob1939.txt        yob1972.txt        yob2005.txt\r\n",
      "yob1907.txt        yob1940.txt        yob1973.txt        yob2006.txt\r\n",
      "yob1908.txt        yob1941.txt        yob1974.txt        yob2007.txt\r\n",
      "yob1909.txt        yob1942.txt        yob1975.txt        yob2008.txt\r\n",
      "yob1910.txt        yob1943.txt        yob1976.txt        yob2009.txt\r\n",
      "yob1911.txt        yob1944.txt        yob1977.txt        yob2010.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls ch02/names/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:30:57.252966Z",
     "start_time": "2019-01-19T02:30:55.894852Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 2010 is the last available year right now\n",
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = 'ch02/names/yob%d.txt' % year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "# Concatenate everything into a single DataFrame\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:30:58.681401Z",
     "start_time": "2019-01-19T02:30:58.366961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1690784 entries, 0 to 1690783\n",
      "Data columns (total 4 columns):\n",
      "name      1690784 non-null object\n",
      "sex       1690784 non-null object\n",
      "births    1690784 non-null int64\n",
      "year      1690784 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 51.6+ MB\n"
     ]
    }
   ],
   "source": [
    "names.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:25:13.232256Z",
     "start_time": "2019-01-19T02:25:12.939544Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                 columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:25:15.426502Z",
     "start_time": "2019-01-19T02:25:15.385476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:25:21.765459Z",
     "start_time": "2019-01-19T02:25:21.442942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13fe7f690>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N6HwCzH/J/GJr9USep13MzOaZX7bi7uzA2AENUaVlQdcN6lTLn3mj2/HEtM08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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123e6f4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:50:14.025406Z",
     "start_time": "2019-01-19T02:50:11.887419Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 942 entries, 0 to 941\n",
      "Data columns (total 5 columns):\n",
      "name      942 non-null object\n",
      "sex       942 non-null object\n",
      "births    942 non-null int64\n",
      "year      942 non-null int64\n",
      "prop      942 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 44.2+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 942 entries, 0 to 941\n",
      "Data columns (total 5 columns):\n",
      "name      942 non-null object\n",
      "sex       942 non-null object\n",
      "births    942 non-null int64\n",
      "year      942 non-null int64\n",
      "prop      942 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 44.2+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1058 entries, 942 to 1999\n",
      "Data columns (total 5 columns):\n",
      "name      1058 non-null object\n",
      "sex       1058 non-null object\n",
      "births    1058 non-null int64\n",
      "year      1058 non-null int64\n",
      "prop      1058 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 49.6+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 938 entries, 2000 to 2937\n",
      "Data columns (total 5 columns):\n",
      "name      938 non-null object\n",
      "sex       938 non-null object\n",
      "births    938 non-null int64\n",
      "year      938 non-null int64\n",
      "prop      938 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 44.0+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 997 entries, 2938 to 3934\n",
      "Data columns (total 5 columns):\n",
      "name      997 non-null object\n",
      "sex       997 non-null object\n",
      "births    997 non-null int64\n",
      "year      997 non-null int64\n",
      "prop      997 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 46.7+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1028 entries, 3935 to 4962\n",
      "Data columns (total 5 columns):\n",
      "name      1028 non-null object\n",
      "sex       1028 non-null object\n",
      "births    1028 non-null int64\n",
      "year      1028 non-null int64\n",
      "prop      1028 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 48.2+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1099 entries, 4963 to 6061\n",
      "Data columns (total 5 columns):\n",
      "name      1099 non-null object\n",
      "sex       1099 non-null object\n",
      "births    1099 non-null int64\n",
      "year      1099 non-null int64\n",
      "prop      1099 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 51.5+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1054 entries, 6062 to 7115\n",
      "Data columns (total 5 columns):\n",
      "name      1054 non-null object\n",
      "sex       1054 non-null object\n",
      "births    1054 non-null int64\n",
      "year      1054 non-null int64\n",
      "prop      1054 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 49.4+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1030 entries, 7116 to 8145\n",
      "Data columns (total 5 columns):\n",
      "name      1030 non-null object\n",
      "sex       1030 non-null object\n",
      "births    1030 non-null int64\n",
      "year      1030 non-null int64\n",
      "prop      1030 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 48.3+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1172 entries, 8146 to 9317\n",
      "Data columns (total 5 columns):\n",
      "name      1172 non-null object\n",
      "sex       1172 non-null object\n",
      "births    1172 non-null int64\n",
      "year      1172 non-null int64\n",
      "prop      1172 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 54.9+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1125 entries, 9318 to 10442\n",
      "Data columns (total 5 columns):\n",
      "name      1125 non-null object\n",
      "sex       1125 non-null object\n",
      "births    1125 non-null int64\n",
      "year      1125 non-null int64\n",
      "prop      1125 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 52.7+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1197 entries, 10443 to 11639\n",
      "Data columns (total 5 columns):\n",
      "name      1197 non-null object\n",
      "sex       1197 non-null object\n",
      "births    1197 non-null int64\n",
      "year      1197 non-null int64\n",
      "prop      1197 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 56.1+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1097 entries, 11640 to 12736\n",
      "Data columns (total 5 columns):\n",
      "name      1097 non-null object\n",
      "sex       1097 non-null object\n",
      "births    1097 non-null int64\n",
      "year      1097 non-null int64\n",
      "prop      1097 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 51.4+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1282 entries, 12737 to 14018\n",
      "Data columns (total 5 columns):\n",
      "name      1282 non-null object\n",
      "sex       1282 non-null object\n",
      "births    1282 non-null int64\n",
      "year      1282 non-null int64\n",
      "prop      1282 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 60.1+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1110 entries, 14019 to 15128\n",
      "Data columns (total 5 columns):\n",
      "name      1110 non-null object\n",
      "sex       1110 non-null object\n",
      "births    1110 non-null int64\n",
      "year      1110 non-null int64\n",
      "prop      1110 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 52.0+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1306 entries, 15129 to 16434\n",
      "Data columns (total 5 columns):\n",
      "name      1306 non-null object\n",
      "sex       1306 non-null object\n",
      "births    1306 non-null int64\n",
      "year      1306 non-null int64\n",
      "prop      1306 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 61.2+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1067 entries, 16435 to 17501\n",
      "Data columns (total 5 columns):\n",
      "name      1067 non-null object\n",
      "sex       1067 non-null object\n",
      "births    1067 non-null int64\n",
      "year      1067 non-null int64\n",
      "prop      1067 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 50.0+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1474 entries, 17502 to 18975\n",
      "Data columns (total 5 columns):\n",
      "name      1474 non-null object\n",
      "sex       1474 non-null object\n",
      "births    1474 non-null int64\n",
      "year      1474 non-null int64\n",
      "prop      1474 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 69.1+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "memory usage: 957.0+ KB\n",
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      "******************************\n",
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      "memory usage: 683.9+ KB\n",
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      "memory usage: 943.3+ KB\n",
      "None\n",
      "******************************\n",
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      "memory usage: 678.7+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19698 entries, 1656946 to 1676643\n",
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      "memory usage: 923.3+ KB\n",
      "None\n",
      "******************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14140 entries, 1676644 to 1690783\n",
      "Data columns (total 5 columns):\n",
      "name      14140 non-null object\n",
      "sex       14140 non-null object\n",
      "births    14140 non-null int64\n",
      "year      14140 non-null int64\n",
      "prop      14140 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 662.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def add_prop(group):\n",
    "    # 对每个group做运算\n",
    "    # Integer division floors\n",
    "    print(\"***\" * 10)\n",
    "    print(group.info())\n",
    "    \n",
    "    births = group.births.astype(float)\n",
    "    # births.sum() 是以['year', 'sex']为分组做的sum\n",
    "    group['prop'] =  births / births.sum()\n",
    "    return group\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:41:01.974077Z",
     "start_time": "2019-01-19T02:41:01.943319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x140226950>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:41:02.816741Z",
     "start_time": "2019-01-19T02:41:02.500669Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1690784 entries, 0 to 1690783\n",
      "Data columns (total 5 columns):\n",
      "name      1690784 non-null object\n",
      "sex       1690784 non-null object\n",
      "births    1690784 non-null int64\n",
      "year      1690784 non-null int64\n",
      "prop      1690784 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 64.5+ MB\n"
     ]
    }
   ],
   "source": [
    "names.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:49:57.365764Z",
     "start_time": "2019-01-19T02:49:57.311832Z"
    }
   },
   "outputs": [
    {
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       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188\n",
       "5   Margaret   F    1578  1880  0.017342\n",
       "6        Ida   F    1472  1880  0.016177\n",
       "7      Alice   F    1414  1880  0.015540\n",
       "8     Bertha   F    1320  1880  0.014507\n",
       "9      Sarah   F    1288  1880  0.014155"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:50:34.845479Z",
     "start_time": "2019-01-19T02:50:34.798890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>prop</th>\n",
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       "  </thead>\n",
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       "      <th>942</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9655</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.087381</td>\n",
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       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>William</td>\n",
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       "      <td>9533</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.086277</td>\n",
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       "    <tr>\n",
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       "      <td>1880</td>\n",
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       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>George</td>\n",
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       "      <td>5126</td>\n",
       "      <td>1880</td>\n",
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       "    <tr>\n",
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       "      <td>3242</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.029341</td>\n",
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       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>Joseph</td>\n",
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       "      <td>2632</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.023821</td>\n",
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       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Thomas</td>\n",
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       "      <td>1880</td>\n",
       "      <td>0.022934</td>\n",
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       "    <tr>\n",
       "      <th>951</th>\n",
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      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "942     John   M    9655  1880  0.087381\n",
       "943  William   M    9533  1880  0.086277\n",
       "944    James   M    5927  1880  0.053641\n",
       "945  Charles   M    5348  1880  0.048401\n",
       "946   George   M    5126  1880  0.046392\n",
       "947    Frank   M    3242  1880  0.029341\n",
       "948   Joseph   M    2632  1880  0.023821\n",
       "949   Thomas   M    2534  1880  0.022934\n",
       "950    Henry   M    2444  1880  0.022119\n",
       "951   Robert   M    2416  1880  0.021866"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[942:952]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:41:06.198646Z",
     "start_time": "2019-01-19T02:41:06.004677Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:47:56.106242Z",
     "start_time": "2019-01-19T02:47:55.936398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:53:38.982810Z",
     "start_time": "2019-01-19T02:53:38.273357Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_last10(group):\n",
    "    return group.sort_values(by='births', ascending=True)[:10]\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "last10 = grouped.apply(get_last10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:53:39.110166Z",
     "start_time": "2019-01-19T02:53:39.026161Z"
    }
   },
   "outputs": [
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       "      <th>866</th>\n",
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       "      <th>867</th>\n",
       "      <td>Glenn</td>\n",
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       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>Gracia</td>\n",
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       "      <th>869</th>\n",
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       "      <th>870</th>\n",
       "      <td>Gwendolyn</td>\n",
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       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000055</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">M</th>\n",
       "      <th>1999</th>\n",
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       "      <td>5</td>\n",
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       "      <th>1902</th>\n",
       "      <td>Flora</td>\n",
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       "      <td>1880</td>\n",
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       "      <th>1900</th>\n",
       "      <td>Firman</td>\n",
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       "      <td>1880</td>\n",
       "      <td>0.000045</td>\n",
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       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>Everette</td>\n",
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       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000045</td>\n",
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       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>Esau</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>Erasmus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000045</td>\n",
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       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>Enrique</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>Ennis</td>\n",
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       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>Ely</td>\n",
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       "      <td>1880</td>\n",
       "      <td>0.000045</td>\n",
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       "      <th rowspan=\"10\" valign=\"top\">1881</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">F</th>\n",
       "      <th>2937</th>\n",
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       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>Eleanore</td>\n",
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       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>Elena</td>\n",
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       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2859</th>\n",
       "      <td>Elodie</td>\n",
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       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>Ena</td>\n",
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       "    <tr>\n",
       "      <th>2861</th>\n",
       "      <td>Epsie</td>\n",
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       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>Erna</td>\n",
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       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>Eugene</td>\n",
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       "    <tr>\n",
       "      <th>2864</th>\n",
       "      <td>Eulah</td>\n",
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       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>Evalena</td>\n",
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       "      <td>1881</td>\n",
       "      <td>0.000054</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th rowspan=\"10\" valign=\"top\">2009</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">M</th>\n",
       "      <th>1656945</th>\n",
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       "      <td>0.000003</td>\n",
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       "    <tr>\n",
       "      <th>1655582</th>\n",
       "      <td>Haroutyun</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655583</th>\n",
       "      <td>Hason</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655584</th>\n",
       "      <td>Hassaan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655585</th>\n",
       "      <td>Hassiel</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655586</th>\n",
       "      <td>Haston</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655587</th>\n",
       "      <td>Havik</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655588</th>\n",
       "      <td>Hawkin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655589</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655590</th>\n",
       "      <td>Hayston</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">F</th>\n",
       "      <th>1676643</th>\n",
       "      <td>Zyrihanna</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674744</th>\n",
       "      <td>Greenlea</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674743</th>\n",
       "      <td>Graysie</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674742</th>\n",
       "      <td>Graycin</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674741</th>\n",
       "      <td>Graice</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674740</th>\n",
       "      <td>Gracelee</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674739</th>\n",
       "      <td>Graceland</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674738</th>\n",
       "      <td>Graceanna</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674737</th>\n",
       "      <td>Gitzel</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674745</th>\n",
       "      <td>Greidi</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">M</th>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689436</th>\n",
       "      <td>Gemari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689437</th>\n",
       "      <td>Geoffery</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689438</th>\n",
       "      <td>Georgiy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689439</th>\n",
       "      <td>Gerron</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689440</th>\n",
       "      <td>Gevon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689441</th>\n",
       "      <td>Gevorg</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689442</th>\n",
       "      <td>Giankarlo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689443</th>\n",
       "      <td>Gianpiero</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689444</th>\n",
       "      <td>Gillian</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2620 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name sex  births  year      prop\n",
       "year sex                                               \n",
       "1880 F   941          Wilma   F       5  1880  0.000055\n",
       "         862          Estie   F       5  1880  0.000055\n",
       "         863          Etter   F       5  1880  0.000055\n",
       "         864        Fronnie   F       5  1880  0.000055\n",
       "         865          Genie   F       5  1880  0.000055\n",
       "         866       Georgina   F       5  1880  0.000055\n",
       "         867          Glenn   F       5  1880  0.000055\n",
       "         868         Gracia   F       5  1880  0.000055\n",
       "         869      Guadalupe   F       5  1880  0.000055\n",
       "         870      Gwendolyn   F       5  1880  0.000055\n",
       "     M   1999     Zachariah   M       5  1880  0.000045\n",
       "         1902         Flora   M       5  1880  0.000045\n",
       "         1901       Fleming   M       5  1880  0.000045\n",
       "         1900        Firman   M       5  1880  0.000045\n",
       "         1899      Everette   M       5  1880  0.000045\n",
       "         1898          Esau   M       5  1880  0.000045\n",
       "         1897       Erasmus   M       5  1880  0.000045\n",
       "         1896       Enrique   M       5  1880  0.000045\n",
       "         1895         Ennis   M       5  1880  0.000045\n",
       "         1894           Ely   M       5  1880  0.000045\n",
       "1881 F   2937         Viney   F       5  1881  0.000054\n",
       "         2857      Eleanore   F       5  1881  0.000054\n",
       "         2858         Elena   F       5  1881  0.000054\n",
       "         2859        Elodie   F       5  1881  0.000054\n",
       "         2860           Ena   F       5  1881  0.000054\n",
       "         2861         Epsie   F       5  1881  0.000054\n",
       "         2862          Erna   F       5  1881  0.000054\n",
       "         2863        Eugene   F       5  1881  0.000054\n",
       "         2864         Eulah   F       5  1881  0.000054\n",
       "         2865       Evalena   F       5  1881  0.000054\n",
       "...                     ...  ..     ...   ...       ...\n",
       "2009 M   1656945     Zyvion   M       5  2009  0.000003\n",
       "         1655582  Haroutyun   M       5  2009  0.000003\n",
       "         1655583      Hason   M       5  2009  0.000003\n",
       "         1655584    Hassaan   M       5  2009  0.000003\n",
       "         1655585    Hassiel   M       5  2009  0.000003\n",
       "         1655586     Haston   M       5  2009  0.000003\n",
       "         1655587      Havik   M       5  2009  0.000003\n",
       "         1655588     Hawkin   M       5  2009  0.000003\n",
       "         1655589  Hawthorne   M       5  2009  0.000003\n",
       "         1655590    Hayston   M       5  2009  0.000003\n",
       "2010 F   1676643  Zyrihanna   F       5  2010  0.000003\n",
       "         1674744   Greenlea   F       5  2010  0.000003\n",
       "         1674743    Graysie   F       5  2010  0.000003\n",
       "         1674742    Graycin   F       5  2010  0.000003\n",
       "         1674741     Graice   F       5  2010  0.000003\n",
       "         1674740   Gracelee   F       5  2010  0.000003\n",
       "         1674739  Graceland   F       5  2010  0.000003\n",
       "         1674738  Graceanna   F       5  2010  0.000003\n",
       "         1674737     Gitzel   F       5  2010  0.000003\n",
       "         1674745     Greidi   F       5  2010  0.000003\n",
       "     M   1690783      Zzyzx   M       5  2010  0.000003\n",
       "         1689436     Gemari   M       5  2010  0.000003\n",
       "         1689437   Geoffery   M       5  2010  0.000003\n",
       "         1689438    Georgiy   M       5  2010  0.000003\n",
       "         1689439     Gerron   M       5  2010  0.000003\n",
       "         1689440      Gevon   M       5  2010  0.000003\n",
       "         1689441     Gevorg   M       5  2010  0.000003\n",
       "         1689442  Giankarlo   M       5  2010  0.000003\n",
       "         1689443  Gianpiero   M       5  2010  0.000003\n",
       "         1689444    Gillian   M       5  2010  0.000003\n",
       "\n",
       "[2620 rows x 5 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:57:37.464506Z",
     "start_time": "2019-01-19T02:57:36.362574Z"
    }
   },
   "outputs": [],
   "source": [
    "pieces = []\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "#     print(year)\n",
    "#     print(group)\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:57:37.561437Z",
     "start_time": "2019-01-19T02:57:37.466917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 261877 entries, 0 to 261876\n",
      "Data columns (total 5 columns):\n",
      "name      261877 non-null object\n",
      "sex       261877 non-null object\n",
      "births    261877 non-null int64\n",
      "year      261877 non-null int64\n",
      "prop      261877 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 10.0+ MB\n"
     ]
    }
   ],
   "source": [
    "top1000.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:57:48.423817Z",
     "start_time": "2019-01-19T02:57:48.384196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188\n",
       "5   Margaret   F    1578  1880  0.017342\n",
       "6        Ida   F    1472  1880  0.016177\n",
       "7      Alice   F    1414  1880  0.015540\n",
       "8     Bertha   F    1320  1880  0.014507\n",
       "9      Sarah   F    1288  1880  0.014155"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:57:53.275461Z",
     "start_time": "2019-01-19T02:57:53.242515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=261877, step=1)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:58:08.029041Z",
     "start_time": "2019-01-19T02:58:07.995400Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top1000.index = np.arange(len(top1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:58:08.896330Z",
     "start_time": "2019-01-19T02:58:08.862596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
       "                 8,      9,\n",
       "            ...\n",
       "            261867, 261868, 261869, 261870, 261871, 261872, 261873, 261874,\n",
       "            261875, 261876],\n",
       "           dtype='int64', length=261877)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:58:09.875818Z",
     "start_time": "2019-01-19T02:58:09.836383Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261877"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:58:10.317344Z",
     "start_time": "2019-01-19T02:58:10.216339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 261877 entries, 0 to 261876\n",
      "Data columns (total 5 columns):\n",
      "name      261877 non-null object\n",
      "sex       261877 non-null object\n",
      "births    261877 non-null int64\n",
      "year      261877 non-null int64\n",
      "prop      261877 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 12.0+ MB\n"
     ]
    }
   ],
   "source": [
    "top1000.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing naming trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T02:59:05.268521Z",
     "start_time": "2019-01-19T02:59:05.162474Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:00:15.066868Z",
     "start_time": "2019-01-19T03:00:15.008641Z"
    }
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   "outputs": [
    {
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       "935    False\n",
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       "Name: sex, dtype: bool"
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   "source": [
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   ]
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       "name  Aaden  Aaliyah  Aarav  Aaron  Aarush   Ab  Abagail  Abb  Abbey  Abbie  \\\n",
       "year                                                                          \n",
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       "1885  ...    6.0  27.0   NaN   NaN  12.0     6.0  14.0  48.0  38.0   NaN  \n",
       "1886  ...   13.0  25.0   NaN   NaN   8.0     NaN  20.0  52.0  43.0   NaN  \n",
       "1887  ...    9.0  34.0   NaN   NaN  23.0     NaN  28.0  46.0  33.0   NaN  \n",
       "1888  ...   11.0  42.0   NaN   NaN  23.0     7.0  30.0  42.0  45.0   NaN  \n",
       "1889  ...   14.0  29.0   NaN   NaN  22.0     NaN  29.0  53.0  55.0   NaN  \n",
       "\n",
       "[10 rows x 6868 columns]"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births', index='year', columns='name',\n",
    "                                   aggfunc=sum)\n",
    "total_births[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:08:22.976151Z",
     "start_time": "2019-01-19T03:08:22.010305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x146254f50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x14459c210>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x14629a0d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12ab656d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12abeb690>], dtype=object)"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lupkZqyqYs6Gaa8YXc8kJBeSnxnX6WJeOLuSCEfk0t/lJjd+7tGR4YSpTxjrbvkCQqsZW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TZU30CooYFQY0P45+JtAn8AGwxiAwFsMID1+gg1NHRInM6sLNy5ueBw0Lx+HcHKKqzf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\nMzMz6wMn1mZmZmZmffD/o0YPxu67JlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146287e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn', 'Aaron']]\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:08:27.021404Z",
     "start_time": "2019-01-19T03:08:26.989451Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Data columns (total 1 columns):\n",
      "John    131 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.0 KB\n"
     ]
    }
   ],
   "source": [
    "total_births[['John']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:10:13.484279Z",
     "start_time": "2019-01-19T03:10:12.380959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x14ba74e10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x16797d990>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x167a02950>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x14e3add10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1685f7390>], dtype=object)"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wh2ljuHhsfqTDEenWjkRiPd5aW9Vh3f1AjbX2PmPMT4A0a+2PjTHDgReACUAe\nMAsYbK0NGGMWArcBC3AS60estTONMbcAo621NxtjrgAusdZOO1hMSqylNympbWHmil08t2AbpXWt\n3H/paC4ZW3DA9v5AEGMMrqieN2Cw0evjw/VVzFpTzuy1FXsNqtzXiLxk/njlWPpnJR7FCL+aNn+A\n37+/gcc/3ERuShwPXnYcJw3IiHRY0gv5AkGuemIBK0rr+df3JzEkR2MdRA7kaCTW64DTrbVlxphc\nYI61dkiotxpr7W9D7d4F7sHp1f4/a+3Q0PorQ/t/d3cba+08Y0w0sAvIsgcJTom1HOt21LQwY0UZ\nM1aUsaykHoCR+cncff5wJvbvHYmYLxBk0dZaFm+rwRPtIjkumqRYN8mxbiqbvPzyzdW0+4P86qKR\nfHPcgd9oREJJbQuzVpezuaqZioY2Khq9VDS2UdHYRrs/yLTxfbn7gmGa/1siqqLBy9cf+Zg+SR7e\n+M9JuFWGJLJfXU2su/qAGAvMMsYEgL9aax8Hsq21ZaHtu4DdnzfnA/M77FsSWucLLe+7fvc+OwCs\ntX5jTD2QAVQh0otYa5m3qZrHP9rMnHWVAIzKT+HH5w3l66NyKMpIiHCER5fbFcVJAzIO2KM7sX8G\nt7+4lLteWcbHG6v41cUjSfRE7rlXmyubmLlyF++u2sXy0Juh5Nho+iTH0ifJw/iiNPokx3LKoExO\nGZQVsThFduuTHMu9F4/k5mcX89e5m/jPMwdFOiSRHq2rf4EmW2tLjTF9gPeNMWs7bgzVSR/xUZDG\nmJuAmwAKCwuP9OlEjhpfIMjby8t44qPNrNrZQEZCDHecPYipYwsozIiPdHjdVm5KHC/cOJE/zt7A\nIx9s4PPttZw1LBuvL0CbP7jn+9CcJK46sYiclNiwnbu5zc/qsgZWltazsrSBpTtq2VTpPJBjTN9U\nfvK1oZw3Iod+mb3rzZD0POeNzOH80bk88sFGzh2Rw6BslYSIHK4uJdbW2tLQ9wpjzOs49dPlxpjc\nDqUgFaHmpUDfDrsXhNaVhpb3Xd9xn5JQKUgKziDGfeN4HHgcnFKQLl2hSDe3srSem55ZxM56LwOy\nErhv6iguHpuvgWxd5Ioy3HH2YE7qn8GPX1vOiwu3h+beduGJjiLaZZi1ppzH5mzi/NG5XD+pmOP6\npu73WP5AkJ11XrZWN7OtpoVtVc3srG/FG5oe0JkmMEij18e2mhZ2F6tlJnoYlZ/M1ROLOHdEDnnd\ncAYWkYNCcIf6AAAgAElEQVT55YUj+HRjFf/16nJe+97JPXJ8hkh30GmNtTEmAYiy1jaGlt8H/gc4\nC6juMHgx3Vr7I2PMCOB5vhi8+AEw6ACDF/9orZ1hjPk+MKrD4MWp1trLDxaXaqzlWBAMWi760yeU\nN3j57dRRnDGkD1H6gxZ226qbefrTbby8aAdNbX7GFaVx8oAMqpraqWxso7KpjarGNsobvHs9aTLW\nHUVeahzxMS480c6DcjzRLuLcLgZlJzIqP4WR+Sn0SfLoyZLS472xtJTbX1zK3ecP4zun9I90OCLd\nStgGLxpj+gOvh15GA89ba39tjMkAXgYKgW040+3VhPb5b+B6wA/cYa2dGVo/ni+m25sJ3BoqI4kF\n/gmMBWqAK6y1mw8WlxJrORa8triEu15Zxu+nHXfQWT4kPBq9Pl5dXMJTn25le00LGQkxZCZ6yEry\n0CcpluxkD/0yEijKiKdfZoISZulVrLXc+MwiPtpQxTt3nEqxyphE9tADYkS6uZZ2P2c8OIec5Fhe\nv2WSeqqPImstgaDVg1hE9rGr3suU389lWG4yL944Uf8viYR0NbHWXxWRCPnr3M2UN7Tx/y4Yrj9e\nR5kxRkm1yH7kpMTy/84fzsItNTz+0UE/OBaR/dBfFpEIKKtv5a8fOoPpxvdLj3Q4IiJ7XDa+gCnD\ns7lv5lp+8NJSmtr8kQ5JpMdQYi0SAQ+8s46ghZ+cNzTSoYiI7MUYw1+uHscdZw/ijaWlXPDIR6wI\nzcsuIgenxFrkKFu2o47pn5dyw+Ri+qZrjmoR6X52T2P5wo0T8fqCTH3sE578aDM9dVyWyNGixFrk\nKLLW8qu3VpOZGMMtpw+IdDgiIgd1Yv8MZt5+CqcN7sO9b69h6mOf8pe5m1i9s0FJtsh+RO7ZvyK9\n0Nsryli0rZb7po4iKdYd6XBERDqVlhDDE9eO49kF23lu/jbum7mW+2aupU+Sh1MGZTFleB/OHpat\nAcEiaLo9kaOmwetjykNzyUjw8Oatk/VkMxHpkXbVe/lwQyVz11fy8YYq6lt9FKbHc9Op/bl0XIGe\nGivHJM1jLdLN3P2vFTy/YDv/+v4kRhfs/5HaIiI9SSBombWmnD/P2cSyHXVkJsbw7UnFXD2xiJS4\n8HwqFwxa1u5qpKqpjVi3C090FB53FLHRLuJjXKTEu/FEK5mXI6uribVKQUSOgkVba3h2/nZumFys\npFpEjhmuKMO5I3I4Z3g28zfX8NjcTTzw7joe+WADI/NTGLX7qyCFAVmJuKIMvkCQlvYAXl+A1vYA\n0S5DnNtFbOgrysCWqmY+3VTNvE3VzNtcTU1z+0HjiHO7SI13kxLnJjnO7RwrOgpP6HtibDQnFqdz\n8sBMklWGJ0eQeqxFjrB2f5DzH/mIlvYA7/3gVBI8ej8rIseulaX1TF9SyorSOlaWNtDqCwAQ44oi\naC3+4MHzDrfL4As4bXJTYjl5QCYnD8igMCOedn+QNn+ANl8Qrz9AU1uAhlYfdS3t1LX4qGv10dDq\nw+sP0uYL0OYP4vUFqGvx0eoL4IoyjCtM47QhWZwyKJP+WYkk6v9k6QL1WIt0E3+du4kNFU3847oT\nlFSLyDFvZH4KI/NTAKdUZHNlEytK61lX3ojLOL3TcTEu4mOiiXVH4Q/aPb3XXl+QVl+AgrQ4Jg3M\npF9GPMZ89fEovkCQz7fXMXd9BXPXV/LAu+t44N11ACTEuMhOjg19eRiUnbSnpz0tIeYrn1t6F/VY\nixxBmyqb+NofPuKcEdk8+h/HRzocEREBKhvbmL+5mp11rZQ3tFHe4KW8wUtZvZfSutY97QrS4hhd\nkMLYvmmcUJzOiLxk3Jr9pFdSj7VIhFlr+dn0FcS6o/j5N4ZHOhwREQnJSvLwjePy9rutvtXHqtJ6\nlpfWs6K0nuUldcxYsQtwarmPL0rlhH7pjMpPIT8tjvzUuEOePtVaG5aeeOl+lFiLHCHPLdjOgi01\n3Dd1FH2SYiMdjoiIdEFKnJuTB2Zy8sDMPesqGrx8trWWz7bWsHBLDQ9/sIGOH/gnxUaTnxpHVpIH\nT3QUblcUMdFRxLiiMAZqmn3UNLdR09xOdXM7TW1+cpJjKcqIp19GAkUZCfTLiGd031TyU+MicNUS\nLkqsRY6Av3+8hV+9vZpJAzO4fHzfSIcjIiJfQZ/kWM4fncv5o3MBp1d7U2UTO+ta2VnXSmltK6V1\nXqqa2qj2B2kPBPEFgrT7gwStJS0+hozEGEalpZKREEOCx0VZvZdt1S3MWlNOVdMXs57kpcQyvl86\nJ/RLY3y/dIZkJxGl5x70GJ0m1saYvsAzQDZggcettQ8bY+4BbgQqQ01/Zq2dEdrnp8ANQAC4zVr7\nbmj9OOApIA6YAdxurbXGGE/oHOOAamCatXZrmK5R5KgJBJ1Hlj/16VbOHZHNH6aN1X+IIiLHmJQ4\nN8cXpnF8YVpYjtfo9bGlqpnPt9excGsN8zdX8+9lOwHITIzh1MFZnD6kD6cOyiQ1XgMqu7NOBy8a\nY3KBXGvtEmNMErAYuBi4HGiy1j64T/vhwAvABCAPmAUMttYGjDELgduABTiJ9SPW2pnGmFuA0dba\nm40xVwCXWGunHSwuDV6U7qal3c9tLyxl1ppybphczM++PkxPVxQRkUNmraWktpUFW2r4KPSUy7oW\nH1EGxhamMWlgJuOK0hhbmKp5uY+SsA1etNaWAWWh5UZjzBog/yC7XAS8aK1tA7YYYzYCE4wxW4Fk\na+38UIDP4CToM0P73BPa/1XgUWOMsT11yhLpdSoavXzn6UWsLK3nlxeO4Fsn94t0SCIi0kMZY+ib\nHk/f9HguHVdAIGhZVlLHnLUVzFlfyaOzNxC0YAwMyU7i+KI0Ruen0D8rkeLMBDITYzQ4MkIOqcba\nGNMPGIvT4zwJuNUYcy2wCLjLWluLk3TP77BbSWidL7S873pC33cAWGv9xph6IAOo2uf8NwE3ARQW\nFh5K6CJHTCBoufZvC9lW3cLj14zn7OHZkQ5JRESOIa4os6f05M5zhtDU5mfZjjoWba1l8fZa3ly6\nk+cXbN/TPtETTXFmAv2zEhicncTg7CSGZCdRkBan8sQjrMuJtTEmEXgNuMNa22CMeQz4FU7d9a+A\n3wHXH5EoQ6y1jwOPg1MKciTPJdJVM1aUsXZXI49cOVZJtYiIHHGJnmgmDcxkUmjmkkDQUlrbypbq\nZrZUNrGlqpkt1S0s2lrLG0t37tkvzu1icHbinmR7cI6TcGcne9TDHSZdSqyNMW6cpPo5a+10AGtt\neYftTwBvhV6WAh2nQSgIrSsNLe+7vuM+JcaYaCAFZxCjSLcWDFoenb2RgX0SOX9UbqTDERGRXsgV\nZSjMiKcwI57TBmftta3R62NDRRPrdzWyrryRDeVNzFlfySuLvygiSI6NZkhOEoNCPduDs5MYkpNE\nup48eci6MiuIAf4GrLHWPtRhfW6o/hrgEmBlaPnfwPPGmIdwBi8OAhaGBi82GGMm4pSSXAv8scM+\n3wLmAZcCs1VfLT3Be6t3sa68kT9MG6OBiiIi0u0kxe5/BpOa5nbWlzd+8bWribeXl/F86xclJanx\n7j1zbBdlJFCcGU9GgocETzQJHhcJMdHEx7hI8ETjiY5Srzdd67GeBFwDrDDGLA2t+xlwpTFmDE4p\nyFbguwDW2lXGmJeB1YAf+L61NhDa7xa+mG5vZugLnMT9n6GBjjXAFV/tskSOPGstj3ywkeLMBC4Y\nrd5qERHpOdITYpjYP4OJ/TP2rLPWUtHYxrpdTrK9tbqZrVUtLN5Wy5vLdhI8SJenK8o4SXaMk3Rn\nJXkY09eZuWRsYWqveVBap9PtdVeabk8ibdbqcr7zzCIeuHQ0l+khMCIicgxr8wcoqW2ltrmd5vYA\nzW1+mtv8tLQHaG73h14HaGl3vpfUtrC6rAFfwMkzC9LiOL4wjZMHZDBpYCZ90+MjfEWHJmzT7YnI\nl1lr+ePsDfRNj+PisQebfVJERKTn80S7GJCVCFmdt93N6wuwamcDn2+v5fPtdXs9+KYwPZ5JAzM4\nsTiDtIQY4twu4mNcxLpdTo93oodoV9QRupojR4m1yGGYu76SZSX1/HbqKNw98B++iIjIkRbrdjGu\nKI1xRU59t7WWjRVNfLKxio83VvPWsjJeWLhjv/u6ogz5qXEUZTjzeRd2/MqI77YPxlFiLXKInNrq\nDeSlxPLN4ws630FEREQwxjAo25l95LpJxfgDQTZVNtPU5qO1PUir74tSkp11rWyvaWFbTQvvrNxF\nTXP7XsdKjXdTmB5PfmocuSlx5KbEkpMSS15qLHmpcWQnxUZkzm4l1iKH6NNN1SzZXsevLhpBTLR6\nq0VERA5HtCuKITlJXWrb6PWxo6aV7TXNbK9pcZLu6hbWlzcyd30lLe2BvdrHREfRNy2OwnRnRpP+\nWQkcV5DKsNzkI/q3W4m1yCHYWNHE/e+uIzvZowGLIiIiR0lSrJvheW6G5yV/aZu1lsY2P2V1XnbW\nt1Ja28qOUOK9raaFhVtqaA4l3jHRUYzIS2Zs3zTGFKYytm8qBWlxYZsqUIm1SCdqmtt5c9lOpi8p\nYVlJPVEGHrj0OGLdrkiHJiIi0usZY0iOdZOc495vD7i1lp31XpZur2PpjlqW7qjj+YXb+PsnWwDI\nTIxhTN9UxvRN5bi+qRSmx5OTEosn+tD/ziuxFtmP5jY/s9aU8+ayncxZV4k/aBmem8zd5w/jwuPy\n6JPcO+bjFBER6emMcQZC5qfGcX7ouRO+QJB1uxr5fEfdnoR71pqKvfbLTIwhNyWOvNSu/81XYi1h\n1e4PErS2R/bmen0B/m9tBW8u38nstRV4fUGykz3cMLmYS47PZ2jOlz9+EhERkZ7H7YpiZH4KI/NT\nuGZiEQD1rT5W7ayntLaVsnovO+ta2VnvZXNlc5eP22MfEBOfP9h+/e6nGJSdyICsRAZlJ5Ea52Zb\nTQtbq5rZWtXMlupmqprayE6KJT8tjoK0OPJT4+mT5KGxzUdVYztVTW1UNrVR09xOSpybvNQ48lKc\nEaW5KXG4ogxNeyZB99PUFiDRE83g7ESKMhK+9BjrpjY/K0vrWV5Sx846L3mpsfRNc6aK6ZsWT0r8\n4U0PU9XUxqKtNbiiohiakxTWeqBwaPMHeHHhDv44eyOt7X5uPLU/3zmlP4meyLx3a/cH94woLm/w\n0tTmp8nrp6nNT2NoudHrc16H1lc1teH1BclMjOHro3K5YHQe44vSIjKqWERERLqPrj4gpscm1rkD\nR9hTfvgkGyoaqW3xfXl7SixFGfFkJcVS3uCltLaVXQ1eAvs8jzMmOoqsRA9pCW7qW32U1XnxH+yZ\nnfvsOyArkcHZiURHRbG8pI6NlU3s/pHGx7i+NEo1PsaF2xVFlIEoYzDGEB1lyE+Lo39mAsVZCfTP\nTKBvejybK5tZsKWa+Ztr2FjRtNdxkjzRDM1NYlhuMn2SPLT7g7QHLO3+IL5A8IvvHZb9QYsn2kWs\nO4pYtwtPdBTxMS76JMWSnRLrTFWTHEtWkof6Vh8761rZVe+lrN5LeYOXPsmxjOmbwoi8lD090v5A\nkOlLSnn4gw2U1rVyYnE66QkxzFy5i8zEGG49cxBXTijcawRuuz/IhopGdtS0EBcTTaInmqRY53uc\n20VtSztVTaE3PY1tVDe3EwgGcYV+XlHG4IqC9oDF6wvg9QVobQ/Q6gtQ1dTGjppWyupb9/voVU90\nFEmx7j3n2/09MTaa9PgYzhjahxOL03vkpPQiIiJyZBzziXXHR5pXN7WxoaKJ+lYfRRnxFKUnEBfz\n5VIEfyBIeaOTrCXHRpOZ5CHJE71Xz28waKlqaqO0zvkYwFpI8LhI8ESTEBNNgsdFXYuP9eWNbKho\ncr6XN9HmDzAqP4Xj+qZyXEEqowtSyEh0EtQdNS2U1LZQEvpoIRC0BO3uLyfR3FHTwuaqZiob2/aK\nOSHGxQnF6Uzsn8GE4nQA1pQ1sLas0fm+q5GmNj/gJPoxrijcLkNMdBRul/N693KUgTa/k2i3+YN4\nfc5jSL2+YKc/b7fL7HksaXSUYVhuMqMKUpi/qZrNVc0cV5DCD88dwuSBmRhjWLqjjvtmrmH+5hoK\n0+P55vEFbKtuZnVZAxsrmrr85gXAGHAZQ8Ba9v11jXVHEed2Eed2ntaUGu+mKCMh9AlB3J4BCMmx\nbhI80ZoeT0RERA5Zr0qsjyWNXh9bqprZVt1CYXo8I/KSD9p7aq3FF7C4XeawSkN2T1Gzp2e63ktF\no5eU+Bjydk+2nhJHarybysY2p8g/VOi/orSegrQ4fjBlMOcMz/7S+a21zF1fyX0z17J2VyN9kjwM\nz0tmWG4yw3OTKc5MoM0f7FCm4aO5LUBqvJusJA+ZiR6ykjykxcfsKbmxoTcjQWuJjjq8axYRERE5\nFEqs5Yiz1nYpsQ0GLU3t/m77+FERERGRg+lqYq3PxeWwdbW3OCrKKKkWERGRY54SaxERERGRMOix\npSDGmFZgVaTjkP0qBLZHOgjZL92b7kv3pvvSvem+dG+6r2Pt3hRZa7M6a9STE+vKrlygHH26N92X\n7k33pXvTfenedF+6N91Xb703PbkUpC7SAcgB6d50X7o33ZfuTfele9N96d50X73y3vTkxLo+0gHI\nAenedF+6N92X7k33pXvTfenedF+98t705MT68UgHIAeke9N96d50X7o33ZfuTfele9N99cp702Nr\nrEVEREREupOe3GMtIiIiItJtKLEWEREREQkDJdYiIiIiImGgxFpEREREJAyUWIuIiIiIhIESaxER\nERGRMFBiLSIiIiISBkqsRURERETCQIm1iIiIiEgYKLEWEREREQkDJdYiIiIiImEQ3VkDY8zfgQuA\nCmvtyNC6dOAloB+wFbjcWlsb2vZT4AYgANxmrX03tH4c8BQQB8wAbrfWWmOMB3gGGAdUA9OstVs7\niyszM9P269ev61cqIiIiInIYFi9eXGWtzeqsnbHWHryBMacCTcAzHRLr+4Eaa+19xpifAGnW2h8b\nY4YDLwATgDxgFjDYWhswxiwEbgMW4CTWj1hrZxpjbgFGW2tvNsZcAVxirZ3WWeDjx4+3ixYt6qyZ\niIiIiMhXYoxZbK0d31m7TktBrLUfAjX7rL4IeDq0/DRwcYf1L1pr26y1W4CNwARjTC6QbK2db51M\n/pl99tl9rFeBs4wxprO4RERERES6k8Otsc621paFlncB2aHlfGBHh3YloXX5oeV91++1j7XWD9QD\nGfs7qTHmJmPMImPMosrKysMMXeQYEvDD69+DFa9GOhIREZFe7ysPXgz1QB+8niRMrLWPW2vHW2vH\nZ2V1WuYicuz77AlY9jy88X2oWHvo++9YCEv+CZ2UhImIiEjnOh28eADlxphca21ZqMyjIrS+FOjb\noV1BaF1paHnf9R33KTHGRAMpOIMYReRgGnbC7HuhaBJUroXpN8J3PoDomM73LV0M//cb2DjLeV2+\nEs67D1SFJSLS6/l8PkpKSvB6vZEO5aiLjY2loKAAt9t9WPsfbmL9b+BbwH2h7290WP+8MeYhnMGL\ng4CFocGLDcaYiTiDF68F/rjPseYBlwKzbWcjKkUE3vkpBP1w0aNQvhpeugrm/BbO/sWB9ylb7rRZ\nNwPi0uHsX0LjLljwGERFwzn3KrkWEenlSkpKSEpKol+/fvSmYW/WWqqrqykpKaG4uPiwjtGV6fZe\nAE4HMo0xJcAvcBLql40xNwDbgMtDAa0yxrwMrAb8wPettYHQoW7hi+n2Zoa+AP4G/NMYsxFnkOQV\nh3UlIr3Jhlmw+l9wxt2Q3t/5GnsNfPx7GDQFik7eu31LDbz7M1j2AsSmOPtNvBk8SU4ZiA3AvEed\n5Prse5Rci4j0Yl6vt9cl1QDGGDIyMvgq4/g6TayttVceYNNZB2j/a+DX+1m/CBi5n/Ve4LLO4hCR\nEF8rzLgLMgbBpNu+WH/eb2HrRzD9u/C9j50EGmDNm/DWndBaA5N/AJPugLjUL/YzBr52v9P7/ckf\nwOWGM+92tlkLVeth4wdQuggyBkLhRCg4wUnKRUTkmNTbkurdvup1H24piIhEykcPQe1WuPbfEO35\nYr0nCaY+AX8/F2b+2CnrmPFfsGo65IyCq1+D3NH7P6Yx8PXfOcn1hw9Aay0E2mHjbGgITeiTlAer\nXgcbBBMF2SOh8CQ48buQMeCIX7aIiPQeiYmJNDU17Xn91FNPsWjRIh599NEIRtU5JdYiPUnVBqdX\nedTl0P+0L2/vOwFO+SF8eD+snQG+Fjjjv52ealcnAzGiouCChyEYgM+eBE8yFJ8Kp94FA86CtCLw\nNjg919vnO19LnnHKSy7+Mwz7xpG5ZhERkUPk9/uJjo4+4OsjRYm1SE/h88JbP4DoODj3S9VWXzjt\nR7DtU/B74cJHIHtE188RFQUX/QlO/SGk9P1yMh6bDAPOdL4A6nbAy9fCS1fDpNvhzJ+DS/+tiIjI\nkfPmm29y77330t7eTkZGBs899xzZ2dncc889bNq0ic2bN1NYWMi5557L9OnTaWpqIhAIUFRUxNSp\nU7n4YucZhVdddRWXX345F110Udhi019AkZ5g3Tvwzo+dEpBvPAyJfQ7c1uWG6946/AGIxjiDIbsi\ntS9c/44zQ8knD0PpErj07wePT0REpBOtra2MGTNmz+uamhouvPBCACZPnsz8+fMxxvDkk09y//33\n87vf/Q6A1atX8/HHHxMXF8dTTz3FkiVLWL58Oenp6cydO5ff//73XHzxxdTX1/Ppp5/y9NNP7/f8\nh0uJtUh3Vr0J3vkJbHgPMgfDNa9/0Vt8MEdz0Em0By54yClDefMO+Msp8M0nofiUoxeDiIgcGTN/\nArtWhPeYOaPga/cdtElcXBxLly7d83p3jTU40wFOmzaNsrIy2tvb95oa78ILLyQuLm7P6ylTppCe\nng7Aaaedxi233EJlZSWvvfYa3/zmN8NeHqLEWiTS2ltg/UzwtzsDA3d/Va2HhY+DK8YZiDjhu117\n+EukHHeFM6Dx5Wvg6Qvg+Gthyv9AXFqkIxMRkWPIrbfeyp133smFF17InDlzuOeee/ZsS0hI2Kvt\nvq+vvfZann32WV588UX+8Y9/hD02JdYikTb/zzD7V/vfNnqak5wm5RzdmA5Xzki4+ROYex98+iis\nm+k80XHkNzU3tohIT9RJz3Ik1NfXk5+fD3DIpRzXXXcdEyZMICcnh+HDh4c9NiXWIpG2cjrkj3fK\nJ0zUF1/uOIhPj3R0hy4m3nkzMPJSePM2eO0GWPYinPsbyBoc6ehERKSHu+eee7jssstIS0vjzDPP\nZMuWLV3eNzs7m2HDhu0ZwBhupqc+PXz8+PF2d62NSI9VuQ7+NAHO+1/nSYjHmmDAKWf54Ffga4bM\nITDkPBj8NacmO8oV6QhFRGQfa9asYdiwYZEO44hoaWlh1KhRLFmyhJSUlP222d/1G2MWW2vHd3b8\nqPCEKSKHZeV0wMDw8E31061EuWDi9+DWxc6bh6QcmPcn+Md58MBAePsu53HrIiIiR9isWbMYNmwY\nt9566wGT6q9KpSAikWKt81TEokmQnBvpaI6s5FynR37izeCtdx6Rvm4GLPoHrP43nP87GH5hpKMU\nEZFj2Nlnn822bduO6DnUYy0SKeWrnJk/Rl4S6UiOrtgUGDnVqSm/aY7Ti/3yNfDSNdBYHunoRERE\nDpsSa5FIWTXdGaQ47BgtA+mK3NFw42w46xew/l2n3nzpC05vvoiIRExPHYP3VX3V61ZiLRIJ1sKq\n16H4/7N33+FRVmkfx78nvRBKCiWEUKSEjoggiIggioiAooiKiqDuqqtiWbu76rrrq65r27WtBRXF\nrhRFqrCAqEDovUgJkg4BUkiZ8/5xQgjSAiSZSfh9ruu5Zuap9+SB5J4z9zmnF9SI8XY03uUfCOfd\nC7fNh7qt4Zs/whejXMmIiIhUupCQEDIyMk675NpaS0ZGBiEhISd9DtVYi3jDzmWQuRnOHePtSHxH\ndAsY+R3MfwlmPQ07FsOV70HcWd6OTETktBIXF0dSUhJpaWneDqXShYSEEBcXd9LHK7EW8YZVX4Ff\nALS+zNuR+BY/P9d63aQnfDEa3r0I+v4Fut/ptomISIULDAw8ZJpwKTv9pRKpbAfKQJpdUDUngKkM\njbrCH+dCqwEw/S/w0ZWQstrbUYmIiByTEmuRyrZjMeze5kbGkKMLrQ3DPoCBL8K2n+D17vDRMNgy\nX50bRUTEJymxFqlsK78C/yBIuNTbkfg+Y6DLKLhnJVzwmPtQMnYAvH2hG/9aCbaIiPgQJdYilcnj\ncWUgzS904zlL2YRFwvl/dgn2pS9ATrob+3rmk96OTEREpIQSa5HKtP1n2PsbtFUZyEkJDIWzb4Y7\nE6HzjTDvxeJp4UVERLxPibVIZVr8HgSEQqv+3o6kavPzhwH/hEbnwIQ7IHmFtyOqXrYvhMQPVWoj\nInKClFiLVJYdibD8U+j2BwiO8HY0VV9AkOvcGFILPrkWcjK9HVHVZy3MfxnevRgm/sl9aCkq8HZU\nIiJVhhJrkcpgLUx9BMJj4Lz7vB1N9RFRD67+CPYmw+cjoajQ2xFVXbm74ZPr3PCGCZdCrz/D0o/g\no6sgb4+3oxMRqRKUWItUhtUTYNsCuOBRCKnp7Wiql7iz3JB8v86BGX/1djRV029L4c1esGEq9P8/\n901An8dg8H9gy1x4bwDs+c3bUYqI+Dwl1iIVrSAPpj8OddtC5xu8HU31dOYI6PoHWPBvmPGkWljL\nqqgAFrwG71wEnkK4aQqcc5sb5hDcz/Xaz2DXr/B2P03SIyJyHEqsRSraz6+7CWEu/rvrdCcV4+K/\nQ4erYd6/4JVOLmEs3O/tqHzXhhnw+rkw9WFo2gv+MNfNePl7zfu6hNtT6BLwRe+5YSNFROQwSqxF\nKtK+VPjfC9CyP5xxgbejqd78A+GKt+CWWVC/vUsYXz0LlnwEniJvR1dx9u+DjE1lT3bT1sG4K+Gj\noW/tM4IAACAASURBVOApgOHj4brPITzq6Mc06AA3z4DYTjB5DLx/GaRvLJ/4RUSqEWOr6HBKXbp0\nsYsWLfJ2GCLHNuluWDIObv8Jolt4O5rTy6YfYMYTsHMpxPeA4R+5iWaqstzdsHgspG+AzM2QuQn2\npbhtteLhzOug07VQO/7Q4/bvhV//B2u/g2XjIagGnP8AdL3Vja5SVta6f8/THnUlTr0fhB53uQ81\nIiLVmDFmsbW2y3H3U2ItUkFSVsEbPV3ycsmz3o7m9GQtLP3YtbLWbgwjvoA6Tbwd1cnJ2gHjhkLa\nGqhRHyKbFS9NIbQ2rJkEm2cDBpr1ho7DXdK9YTps+8m1TgfVcOUyFzwC4dEnH8veZJjygOuUW7cN\ndLzGlYzUbXOwPltEpBpRYi3iTUUFMPZS97X7XUuqfktpVbdlPnxyDfgHw7WfQsPO3o7oxKSucUl1\n3h7X8t7s/CPvt2ura5Fe8hFkbXPr6rZ1SW+Lfm5CnRNpoT6eNZNh1tMu2QeIaABn9HFLVHOIqO+G\nmFTfAhGp4iolsTbGbAH2AkVAobW2izEmEvgUaAJsAYZZa3cV7/8wMLp4/7ustVOL158FjAVCge+A\nu+1xAlNiLT5t6qNuhIqh70D7K70djcDB2uKcdLhqLLS82NsRlc3WBTD+aggIgeu+cPXOx+PxwI7F\nUDMWajWs+BizkmDTLNg407Wa5+0+uM34ueQ6or7ra9Dtj/qgKSJVTmUm1l2steml1j0HZFpr/88Y\n8xBQx1r7oDGmDTAe6ArEAjOAltbaImPML8BdwM+4xPoVa+2UY11bibX4rNUT4bPr4exb4NJ/ejsa\nKW1vMnw8zE2B3vUPrhY5tDaE1oGQ4sfQ2u55YIi3o3XlHV+MhtqNYMRXUKextyM6Pk8RpKyE3dth\nXzLsTYG9O2HXFjcmdlANOHs0dP8T1Kjr7WhFRMrEm4n1OqC3tXanMaYBMNta26q4tRpr7TPF+00F\nnsC1av9grU0oXn9N8fF/ONa1lViLT8rYBG/1dl+Dj/oeAoK9HZH83v598PUfYO3kY+8XEOqS7PBo\nV05Rv50bbaRe+2OPoFEe8nPcsIFzX4DYzm4s6Yq+ZmVIWe3e06qvwD8IzhoJ546Bmg28HZmIyDGV\nNbEOOMXrWGCGMaYIeNNa+xZQz1q7s3h7MlCv+HlD4KdSxyYVrysofv779YcxxtwK3AoQHx9/pF1E\nvKcgFz670dWTDntfSbWvCq7h6pQ9RZCX5coWcne5ETdKP8/d5V7vTXazOi7/5OA5ImJdkn0g2a7f\nAeo0Bb9THMHUWpfwf/+Iq5FuPwwuewmCwk/tvL6iXhu48h3o/TDMexEWvu3qwS96CjqPPPWfn4iI\nl51qYt3TWrvDGFMXmG6MWVt6o7XWGmPKrXdkceL+FrgW6/I6r0i5+O7PkLLC1cH+frgz8T1+/q7W\nt6z1vtnproQkeYUrdUheARtngC0eIzsgFAJD3WtrXeJuPRB1BjTp6ZbG5x79eukb3Egbm2a50TVG\nfuuOqY6im8OQ/8B597oRWybfAyu/gstedj8vEZEq6pQSa2vtjuLHVGPM17j66RRjTINSpSCpxbvv\nABqVOjyueN2O4ue/Xy9SdSwZB0s+hF5/dqMvSPUTHu0m+Sk90U9BHqStdUl26hooyncJu/FzC7gk\nfPH78PMb7nW9dm7oP0+B27+o0D3fkegS8/7Pwtk3g/+ptntUAVFnwA0T3f+dqY/B6z3cUIDn3HF6\nvH8RqXZOusbaGBMO+Flr9xY/nw48BfQFMkp1Xoy01j5gjGkLfMzBzoszgRZH6bz4qrX2u2NdXzXW\n4jM8RfDPlhDdEkZO1tBicrjCfPgt0XXe2zIPsjNc4ugX6CZX8Qtw/37Of+D07dC3Zyd8ex+s+xYi\nz4B2V0Cbwe6DiMbGFhEvq/DOi8aYZsDXxS8DgI+ttX83xkQBnwHxwFbccHuZxcc8CowCCoExB0b+\nMMZ04eBwe1OAOzXcnlQZWxfAe/3hyneh3VBvRyNSdVkLaybCL/+FrfNdKU3kGdBmELQZAg06KskW\nEa/QBDEilWXa4/DT6/DAZgip6e1oRKqHfWmuI+fqCW46dlvkWvU7DIP2V1XdGTRFpEpSYi1SWV49\ny3VWvP7r4+8rIicuJ9Ml2Cs+dy3Z4GaR7HAVtB50+pbPiEilqazh9kROb+kbIGOjm01ORCpGWCR0\nucktu7e5BHv5Z64m+9v7Ib67KxdpfRnUijv++UREKogSa5FTsfZb99jqEu/GIXK6qB0P590HPe+F\n1NVuptM1E+H7h9wS29kl2rFnuiWymcbHFpFKo8Ra5FSs+85NDqJWMpHKZQzUa+uWCx6G9I2wZgKs\nnwqL3oHCPLdfcE3X6fGMPq5FO7qFd+MWkWpNNdYiJ2tfGvyzBfR+yC0i4huKCt344r8tcUvSQkhe\n7rZFt4SEgdB6oGvd1igjIlIGqrEWqWjrvwcstBrg7UhEpDT/gOLp5ttB5+vduqwkWPsdrJ0E81+G\nef+Cum2h1/1uvGyNPy8i5UCFZyIna90UqNUI6rf3diQicjy14qDbrXDjJPjzRhj0qpvx8oub4LXu\nrjNkUaG3oxSRKk6JtcjJyM+BTbNcp0V9lSxStYRFQucb4Paf4Mr3XGv1V7fAf86GRe9Bfra3IxSR\nKkqJtcjJ+HUOFOZqNBCRqszP302d/sf5cPU4CI6AyWPghdbw/SOQscnbEYpIFaPEWuRkrP3WjTbQ\nuKe3IxGRU+Xn50YMuXUO3DQFmveFX96EVzvDh1e4si9PkbejFJEqQJ0XRU6Up8h1XGzRDwKCvB2N\niJQXY6BxD7fsTYbF78Oid2H8cDd+dpfRroQkLNLbkYqIj1KLdUXJ3eVaOfbv9XYkUt52LIbsNI0G\nIlKdRdSH3g/CPSvhqrFQKx5m/BVeSICvb4OtC1xfCxGRUtRiXRHy9sAHg2HnMvAPdhMTtBkELfur\npaM6WPst+AVA8wu9HYmIVDT/QGh7uVtSVsPC/8KyT2HZx4CBqDOgbhuo185NVhN7JtSMVadmkdOU\nEuvyVrgfPr0OklfCJc/Drl9hzSRYP8UlY03Oc0l2wkCoUdfb0cqJKsyHtZOh8bkQWtvb0YhIZarX\nBga+CBc+AZtnu0Q7ZaVb1kwCiidcq1GveEr1zu4xppUb7k9jZYtUe5p5sTx5ityYqKsnwJA3oNM1\nbr218FsirJ4IayZC5mbAQHx3l2S3vuzYU2J7imDlVzD/JfeLue3l0GYIRDatlLclxfL2wGfXuz+o\nQ9+B9ld6OyIR8RX52S7R/i0RdiS6x/QNlCTb/kEQ2QyimrtW7oSB0KirV0MWkbIr68yL1Tuxthay\n0yE8+shfy1kLKatg1dewcTqERUODjgeXOk3ccYX5rqY2OxX2pUJ4jGuFKH1Oa+Hb+2DRO3DR09Dj\nzqPHlLr6YJKdutqtjzvbJcttBkPtRm6dx+P2mf2Mm563blsIDIUdxe879kyXZDc5z9UDhse4ry29\nKXkF5O6Gpud5N47ylrUDPh7m7sOgV6HTtd6OSER8Xd4e9zsxY4Mbui9jE2Ruco0rRfkQ19X9rUi4\nVK3ZIj6u+ifWrZvYRbO/g5gEN1RSaekbYcVnsPxT2LUFQmq72fEadIT6HVzr8KZZsPobyNgIxs+1\nHu/fA6lrwFM8+1ZwLXfu3F2HBxARCwkD3C/Exj3d9Lizn4Eed8FFfyv7G0nfCGsmwKpvIHm5W9ew\nC7S4yH21mLIColtC74dd4u3nB7u2ulbxVV+7VpHSQuu4ryHDY1ypSenn4TEQUsuN1RpcE0JqQlDE\n4T+/37PWxVY73p3/aJZ/DhPucLOZXfketB1S9p+DL0teCR9d5TqiXv2Bq5kXETlZ+/fB0o9gwX9g\n91ao0xS63wHthqofjoiPqv6Jday/XXRrDZcoNjoH4s+BgBBY+YUbtQEDzc6HZhe4Ouedy13rcGGe\nO4Hxcy29bYdAwmVQI8atL9zv9tu5zB0DLjmtUbc4Oa3rkvF138LGmVCQ45LT/L3Q6ToY/J+T77SS\nsckl+weS7MhmcP5DruTgaK0Zu7a4Vvd9KbDvQKt66eepkL/v6Nf0D3I/ozaD3QeF0onz3mRY+jEs\n+dC1sIRGutb4Ttce3lo/+xmY86yrPfYUuq9CrxnvhqSrqjwe2DwLPr8JgmrAdZ9D/XbejkpEqgtP\nkWtA+fHVg99E1oxzv2fqtXOP9Tu4xPt4DSAiUqGqf2Ldqb1d9P6jsG0BbPvJfUUPrmW6w9XQ7kqo\n2eDQg4oKIX29S7Tjuh5Mpk9WQa6rt1072SWolzwP/uXUH3Rfqktky+N8+dnufDkZkJflWubz9rgW\n2KwkN8pF1jbXubJZbzijL2yZC+ungi1yyXK7K2D5Z7D9Z9dCP/BfrkNOQS58czus+go6jXAdewpy\n4P2Brr5wxFfQ5NxTfw+n6kD9Y/Iy99Xsrq3uw5VfgPvQcuCDS84uyEl3JUS5mWA9rgTnus+hVkPv\nvgcRqZ6shaRFsHW+6wiZvNL9rbLFk9IEhruOk/Xaub9x9dpCVAsIj/Ju3CKnkeqfWP++xjonE/J2\nu1ZeOTElnSuLS1J2b3Ut852uhTOvh+jmbj+PB5Z8ANP/6hLVHn+CX+e6bwgufALOvftgS3Z2Orx3\nCezZCTdOhIadD71m7i7XQl+78dFr4A/weE68tSYvy33o2TDdfRgo3YkopLbrPASuxchT5P6AWeta\n7MOjXL19eLT7tqLDMPfNiIhIZSnIg7Q1Lsk+kGynrHC/2w4IrVPcGbKFa+ho1M39rg0I9l7cItXU\n6ZdYS/mw1pWX1Io7ekfIfWkw7VFXwx4YBle85UY2+b2sHfBef9cyPvxj12q+9cfiVplVlCS6oXUg\nuhXEtHRfeeZmupb0rCR3jr07XZIb3cr98Yhp5erOgyOgqMCVnngK3DcSKStgwwyXTNui4mnHzy3u\nkNrBtfbUaqQxZkWk6rHW/V5MXeM6RKZvcKWJ6RtgX7Lbxz8YGp4FjbtDg04uEd+zwy1ZO9zv4bqt\n3beTzXrrmziRMlJiLRVv+0LXkhvT8uj7ZG6Gdy85+Es/MMwNMdW4p/vlnrXdlfGkrXePuZnuD0Ot\nuOKlkRvxZF+y2yd93aEtNkdSvz007+fqu+PO9v5IKSIiFS073ZVFblvgGjB2LjtYSgLuW8haDSEs\nym3LTnPro1q4BLvFRdC0FwSGeCN6EZ+nxFp8R+avsHGGaz2J7XTsRHf/XtdR8Ggtyta6zpnp6119\nt1+AO59foHusFecScRGR01l+tmusCIuCiAaHloccGPZ182y3bJkPBdmulrt5H2h1KbS8WCOUiJSi\nxFpERESOr3C/6y+z7ltYN8WV3xm/Q+d1aNCxeC4FtWjL6UmJtYiIiJwYa+G3JbDuO9j+iysbydvt\nthl/V8JXOtmu1w6Ca3g3ZpFKUNbEupzGhhMREZEqzxg3ssiBkZysdSNF7VxePL/DMtgwzU1w4w6A\nmg0hIMiV5B0YwjQwzI2+FN3STeQW09KNAqUZJqWaU2ItIiIiR2YM1GniljaD3Dpr3QRiBxLtXb+6\n0ZlKRmkqdLNLbpxRKgHHJd5B4W4yt4Dgg49BNdxMwAdmBQ6OcAk61l3rwGNgmJt/Irx4JuEaMW6+\nh8Aw18dGoz2JD1BiLSIiImVnjJuArWYDaNX/2Pvm7nLDAaatdaNE5ee4GZAL9xc/5rkkfM9vbvKy\n/XvdBGaeQncd4wcY97wo/xgx+UNgqFuCargOmxH13WPNBm5OgsCww5P6Iz3m73MTqpVegmu61veo\nM9w1TkVelhvFpVYj19Iv1YoSaxEREakYoXXcEKuNup76uQrz3cy4+1JdYpqd6hL3gtyDS2GuS8wP\ntKiv/97NBlxuDNRp7JLs0EiXhO/f6x7zs90HggMt7yE1IbiWa03f81vx3Azb3QcIcC34dRPctPUH\nZtQMCHXDJHoKiycwKzw4iVnpdcERLjGv3cg9F5+hxFpERER8X0AQ1Ix1S1lZ6xLZfWkuwS5pKd/v\nkvDSLecHngfVcMMUhkW6x9BI14EzfX1x6/s69zx1reu4GVTDzelQs6ErYdm/xyX3+1LcY9F+iIh1\nCXmTc92wsGFRbnKf5BVuhuDSJTMnKqS2S7JrxLh5IAKCDj4GhEJobfcBJ+TAYy1XkhNUA4LCXEt+\nUHjZ5nzweIq/SVDZzdEosRYREZHqyRiXSIbUOsUTNXKtyhVlb4obW9xT6Dp4+gW48pYDnUEPWefv\nykmytsPu7QcfczJcuUxRvvuQUJTvPkzkZYH1HD8G/yCXYAeGH0y4iwoOtsYX5Bxs/fc/QhlNYEjx\n8+LXxv9gPAcWT5G7TkDwoY/WU6pl/sCj53evSz2Wfh4Y6j4whNY5+CHCL9DFmr/PlR/lZ7u4a9Qt\nLhOq5x7Dot2/EespXopr+v2DDy8RKiOfSayNMf2BlwF/4G1r7f95OSQRERGRihdRzy0noqzlNR6P\na0XP3eVa3nN3H0yU87OP/rwg52CyfWAJDHfnPKRO/vet//vdtTyFLkH1D3LlKv5BB5PtA98S5GW5\n5N34gZ/fwQ8OptSHiYDgQz9UGL9D9ynIde8p81d33dxd7hpBNUrFHuaS5p3LXAlRWT5onCSfSKyN\nMf7Af4B+QBKw0Bgz0Vq72ruRiYiIiFRhfn7FLbm1vR2JbygqdLX6e3dCdgYYSnWS9Svep+DwEqEn\nby7T6X0isQa6AhuttZsBjDGfAIMBJdYiIiIiUj78A4pHjKl/ggeWLbH2O/GIKkRDYHup10nF60RE\nREREqgRfSazLxBhzqzFmkTFmUVpamrfDEREREREp4SulIDuARqVexxWvO4S19i3gLQBjTK4xZlXl\nhCcnKB7Y5u0g5Ih0b3yX7o3v0r3xXbo3vqu63ZvGZdnJWGsrOpDjB2FMALAe6ItLqBcC11prj5o4\nG2PSrLUxlRSinADdG9+le+O7dG98l+6N79K98V2n673xiRZra22hMeZPwFTccHvvHiupLra74iOT\nk6R747t0b3yX7o3v0r3xXbo3vuu0vDc+kVgDWGu/A747gUOyKioWOWW6N75L98Z36d74Lt0b36V7\n47tOy3tTpTov/s5b3g5Ajkr3xnfp3vgu3RvfpXvju3RvfNdpeW98osZaRERERKSqq8ot1iIiIiIi\nPkOJtYiIiIhIOVBiLSIiIiJSDpRYi4iIiIiUAyXWIiIiIiLlQIm1iIiIiEg5UGItIiIiIlIOlFiL\niIiIiJQDJdYiIiIiIuVAibWIiIiISDkI8HYAJys6Oto2adLE22GIiIiISDW3ePHidGttzPH2q7KJ\ndZMmTVi0aJG3wxARERGRas4Ys7Us+6kURERERESkHCixFpEqJ7sgmw9WfUBydrK3QxERESmhxFpE\nqpSlqUu5cuKVPL/oea759hpWpa/ydkgiIiJAFa6xFpHTS6GnkP8u/y9vLn+T+uH1+UfPf/DvJf9m\n5Pcjeea8Z7iw8YVHPK7IU4S/n38lRysiUj0UFBSQlJREXl6et0OpFCEhIcTFxREYGHhSxyuxFhGf\nt33vdh6e+zDL0pYxsNlAHun2CBFBEXSP7c7ds+7mntn3cM9Z93BT25swxmCtJTE1kW82fsO0LdNo\nXLMxz5//PI1rNvb2WxERqVKSkpKIiIigSZMmGGO8HU6FstaSkZFBUlISTZs2PalzKLEWEZ+2KmMV\no6eOxg8/nj3vWQY0G1CyLTo0mncufofH5j/Gi4tfZEvWFuIi4piwcQLb9m4jLCCMC+IvYN6OeVw9\n+Wqe7PEkFze52IvvRkSkasnLyzstkmoAYwxRUVGkpaWd9DmUWIuIzyrwFPCX+X8hPCCcDwd8SGyN\n2MP2CQkI4blez9G4ZmPeWv4WAF3qdeHWDrfSr3E/wgLD2LlvJ/f/737un3M/i1MWc3+X+wnyD6rs\ntyMiUiWdDkn1Aaf6XpVYi4jPen/V+6zftZ6XL3j5iEn1AX7GjzvPvJML4y+kRlANGkU0OmR7gxoN\nGHvxWF5KfIkPVn/A8rTl/PP8fxIXEVfRb0FERE4jGhVERHzSlqwtvL70dfo17kef+D5lOqZ1VOvD\nkuoDAv0D+fPZf+al3i+xbc82rph4Be+vep9CT2F5hl0uUrJTeHTeo1w9+WqyC7K9HY6IiFcZYxgx\nYkTJ68LCQmJiYhg4cKAXozqyCkmsjTEhxphfjDHLjDGrjDFPFq+PNMZMN8ZsKH6sU+qYh40xG40x\n64wxKoIUOY15rIcnFzxJcEAwj3R7pFzP3bdxX74Y9AVn1z+bfy76J1dPvpqlqUvL9RonK7cwl9eX\nvs5l31zGlF+nsDpjNWNXjfV2WCIiXhUeHs7KlSvJzc0FYPr06TRs2PCEzlFYWDmNKBXVYr0f6GOt\n7Qh0AvobY84BHgJmWmtbADOLX2OMaQMMB9oC/YHXjDEaH0vkNPXVhq9YlLKI+7vcT3RodLmfP7ZG\nLP/u829e6v0SWfuzuH7K9Tzx4xNk7c8q92uVhcd6mLRpEgO/Hshry17jvIbnMXHIRC5qfBHvr3qf\n9Nx0r8QlIuIrBgwYwLfffgvA+PHjueaaa0q2/fLLL3Tv3p0zzzyTHj16sG7dOgDGjh3LoEGD6NOn\nD3379uWGG27gm2++KTnuuuuuY8KECeUaZ4XUWFtrLbCv+GVg8WKBwUDv4vXvA7OBB4vXf2Kt3Q/8\naozZCHQFFlREfCLiu1JzUvnXon/RtX5XLm9+eYVdxxhD38Z96R7bndeXvc6Hqz9k7o65fHLpJ8SE\nxVTYdY/kiR+f4OuNX9Mmqg3P9XqOs+qdBcDdne9m1rZZvL70dR7v/nilxiQi8nvP/vIsazPXlus5\nEyITeLDrg8fdb/jw4Tz11FMMHDiQ5cuXM2rUKObOnevOkZDA3LlzCQgIYMaMGTzyyCN8+eWXACQm\nJrJ8+XIiIyOZM2cOL774IkOGDCErK4sff/yR999/v1zfT4XVWBtj/I0xS4FUYLq19megnrV2Z/Eu\nyUC94ucNge2lDk8qXvf7c95qjFlkjFl0KkOhiIjveubnZ8j35POX7n+plJ7oYYFh3NflPsYNGMfe\n/L3cP+d+CjwFFX7dA+Zsn8PXG7/mxjY3Mv7S8SVJNUB8zXiubHklX274kl+zfq20mEREfE2HDh3Y\nsmUL48ePZ8CAAYdsy8rK4qqrrqJdu3bcc889rFp1cEbefv36ERkZCcD555/Phg0bSEtLY/z48Qwd\nOpSAgPJtY66wUUGstUVAJ2NMbeBrY0y73223xhh7gud8C3gLoEuXLid0rIj4toKiAt5e8TYzts1g\nTOcxlT6ZS7vodjzR/QkenPsg/1r0r6O2oKTlpPHpuk+JrRFL57qdaVyz8Ul/ANiTv4enFjxFizot\nuLvz3fiZw9s6/tjxj0zcNJFXEl/hxQtePKnriIiUh7K0LFekQYMGcf/99zN79mwyMjJK1j/++ONc\ncMEFfP3112zZsoXevXuXbAsPDz/kHDfccAPjxo3jk08+4b333iv3GCt8uD1r7W5jzA+42ukUY0wD\na+1OY0wDXGs2wA6gdFf+uOJ1InIaWJi8kKd/eprNWZu5uMnF3ND2Bq/EMaDZAFakr2DcmnG0j25/\nyGQ0AMvSlnHvD/eSmptasi4yJJLOdTvTuV5n2ka1pVVkK8IDw39/6iN6fuHzZORl8ErfVwj0P/L0\nuVGhUYxsO5LXlr3GsrRldIzpePJvUESkChs1ahS1a9emffv2zJ49u2R9VlZWSWfGsWPHHvMcI0eO\npGvXrtSvX582bdqUe4wVklgbY2KAguKkOhToBzwLTARuBP6v+PFAxfhE4GNjzL+AWKAF8EtFxCYi\nviMzL5MXFr3AxE0TaVijIf/p+x96xfXyakz3drmX1RmreWLBE7So04IWdVoA8PWGr/nbT3+jblhd\nvrjsCwL9AlmcupglKUtITE1kxrYZJeeIj4gnITKBhMgELm5yMfE14w+7zrwd8/hm4zfc3P5m2ka1\nPWZMN7a9kU/Xfcq/Fv2Lsf3HnlaTNYiIHBAXF8ddd9112PoHHniAG2+8kaeffppLL730mOeoV68e\nrVu3ZsiQIRUSo3H9DMv5pMZ0wHVO9MfVcX9mrX3KGBMFfAbEA1uBYdbazOJjHgVGAYXAGGvtlGNd\no0uXLnbRokXlHruIVLycghy+3PAlbyx7g5zCHG5qexO3dLiF0IBQb4cGuHKPYZOHER4YzoeXfMhr\nS1/jk3Wf0L1Bd57r9Ry1Q2ofdkxqTiprMtawNnNtyZK0L4kgvyBGtx/N6PajCfYPBmBf/j6GTBhC\neGA4n132Wcn6Y/l07ac8/fPTvNrnVXo36l3eb1lE5IjWrFlD69atvR1GucnJyaF9+/YkJiZSq1at\nI+5zpPdsjFlsre1yvPNXSGJdGZRYi1Q9Wfuz+Hjtx3y85mN2799NtwbdeKTrIzSr3czboR0mMSWR\n0VNHExwQTHZBNiPbjuTuzncT4Ff2L/pSslN4YdELTNkyhfiIeB7t9ig9GvbgyQVP8tWGr/jwkg/p\nENOhTOcq8BRw+YTLCTABfD7ocwL9jlw6IiJSnqpTYj1jxgxGjx7NPffcw5gxY466nxJrEfFpqTmp\nfLDqAz5f/zk5hTn0btSbm9vf7PP1wp+s/YRXEl/h0XMe5dJmx/568Vh+/O1H/vHzP9i6ZyvdG3Rn\nwc4FjGw7kvu63HdC55m5bSZjfhjDJU0v4Zmez+Dvp+H+RaRiVafEuqxOJbGu8M6LInL62rpnK++t\nfI+JmybisR76N+3P6HajS+qWfd3whOEMazXsiKN1nIgesT34ctCXvLviXd5e8TZNajbhjk53nPB5\n+sb3ZUznMbyU+BLB/sE82ePJU45NROR4rLWnTd+OU21wVmItIuVubeZa3lnxDtO2TiPABHBFiysY\n2XYkcRFx3g7thJVX4hrsH8xtnW5jaMuhBPoFEhIQclLnGd1+NHlFebyx7A1C/EN4pNsjp80ff9Qb\nkgAAIABJREFUPBGpfCEhIWRkZBAVFVXtf9dYa8nIyCAk5OR+P4MSaxEpR9ZaHpv/GBM3TSQ8MJyR\nbUdyfZvrK2Ra8qqqbljdUz7H7R1vJ68wj7GrxhISEMK9Z91b7f/giYh3xMXFkZSUxOkyMV9ISAhx\ncSffCKTEWkTKzezts5m4aSLXJlzLHWfeQc2gmt4OqVoyxnDvWfeSW5jL2FVjCfYP5qZ2NxEaEKrS\nEBEpV4GBgTRt2tTbYVQZ6rwoIuWiwFPAFROuwBjDl4O+1KgVlcBjPfxl/l+YsGlCybrQgFBCA0Kp\nEViDh7o+xHlx53kxQhGR6kGdF0WkUn2x/gu27NnCq31eVVJdSfyMH0/2eJIesT1Iy00juyCbnIIc\ncgpz+GH7D7y78l0l1iIilUiJtYicsr35e3l96et0rd+V8+PO93Y4pxV/P//Dpl4HqB9en1eXvMr2\nvdtpFNHIC5GJiJx+VIwnIqfs7RVvs3v/bu7rcp860fmIQWcMwmCYsHHC8XcWEZFyocRaRE7Jb/t+\nY9zqcVx2xmW0iWrj7XCkWP3w+nSP7V4yhriIiFQ8JdYickpeTnwZYwx3nnmnt0OR3xl8xmB2Zu/k\nl+RfvB2KiMhpQYm1iJy0lekr+e7X77ihzQ3UD6/v7XDkd/rE9yEiMELlICIilUSJtYiclEJPIc8v\nfJ7IkEhGtx/t7XDkCEICQrik6SXM2DqDvfl7vR2OiEi1p8RaRE5YQVEBD/zvARJTExnTeQzhgeHe\nDkmOYnDzweQV5TF1y1RvhyIiUu0psRaRE5JbmMudP9zJ9K3TeeDsB7i8xeXeDkmOoX10e5rVaqZy\nEBGRSlAhibUxppEx5gdjzGpjzCpjzN3F6yONMdONMRuKH+uUOuZhY8xGY8w6Y8zFFRGXiJyaffn7\nuG3Gbfy440ee7PEk17e53tshyXEYYxjSfAhL05bya9av3g5HRKRaq6gW60LgPmttG+Ac4A5jTBvg\nIWCmtbYFMLP4NcXbhgNtgf7Aa8YY/wqKTUROwu683dw87WaWpS7juV7PcUWLK7wdkpTRwGYD8Tf+\narUWEalgFZJYW2t3WmsTi5/vBdYADYHBwPvFu70PDCl+Phj4xFq731r7K7AR6FoRsYnIidu5byc3\nTb2JDbs28NIFL9G/aX9vhyQnICYshnMbnsukTZMo8hR5OxwRkWqrwmu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Fzw2wy1pb2xjz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Zd2RNdFREQiZf2ybTxz70LqdjRxxtWjmDitokPP1RBPxhg0uoxBo8s48ePDWTGvindfWMOrDy/j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\n0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cf52ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boys_births = boys.pivot_table('births', index='year', columns='name',\n",
    "                                   aggfunc=sum)\n",
    "subset = boys_births[['John', 'Harry', 'Mary', 'Marilyn', 'Aaron']]\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of boys births per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:10:15.826176Z",
     "start_time": "2019-01-19T03:10:14.724355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x167896b10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x167cce6d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1682d5690>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1668cb310>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1669502d0>], dtype=object)"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2F5Qx9boEhner3RkCThXvL9/NX2av48oh7Zm1Yg9Xn9aBJ8b3OfiP5qer93LP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16792d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "girls_births = girls.pivot_table('births', index='year', columns='name',\n",
    "                                   aggfunc=sum)\n",
    "subset = girls_births[['John', 'Harry', 'Mary', 'Marilyn', 'Aaron']]\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of girls births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measuring the increase in naming diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:10:18.522722Z",
     "start_time": "2019-01-19T03:10:18.483619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ba88fd0>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ba88fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:12:12.329582Z",
     "start_time": "2019-01-19T03:12:11.780615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x167040910>"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fas7w7O6PEATk2Y53uPtmIF3KiomIiIiILDbq8ywiIiIiUqR5Dc9mdpuZbTGzLZ2dnfN5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\neDYzMzMzq8jFs5mZmZlZRf8Da7Tg1i5oO20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1430f7d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                            columns='sex', aggfunc=sum)\n",
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "           yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:12:16.876369Z",
     "start_time": "2019-01-19T03:12:16.833317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.998702</td>\n",
       "      <td>0.995646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.997596</td>\n",
       "      <td>0.998566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.993156</td>\n",
       "      <td>0.994539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>0.992251</td>\n",
       "      <td>0.995501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>0.989504</td>\n",
       "      <td>0.995035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>0.988279</td>\n",
       "      <td>0.996697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>0.984241</td>\n",
       "      <td>0.992429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>0.984061</td>\n",
       "      <td>0.994981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  1.000000  0.997375\n",
       "1881  1.000000  1.000000\n",
       "1882  0.998702  0.995646\n",
       "1883  0.997596  0.998566\n",
       "1884  0.993156  0.994539\n",
       "1885  0.992251  0.995501\n",
       "1886  0.989504  0.995035\n",
       "1887  0.988279  0.996697\n",
       "1888  0.984241  0.992429\n",
       "1889  0.984061  0.994981"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:25:05.463777Z",
     "start_time": "2019-01-19T03:25:05.419158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:25:08.368130Z",
     "start_time": "2019-01-19T03:25:08.331314Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 260877 to 261876\n",
      "Data columns (total 5 columns):\n",
      "name      1000 non-null object\n",
      "sex       1000 non-null object\n",
      "births    1000 non-null int64\n",
      "year      1000 non-null int64\n",
      "prop      1000 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 46.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:17:32.427882Z",
     "start_time": "2019-01-19T03:17:32.397970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()  # 累计和\n",
    "\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:18:14.038208Z",
     "start_time": "2019-01-19T03:18:14.010158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:19:13.483883Z",
     "start_time": "2019-01-19T03:19:13.454923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:36:38.608742Z",
     "start_time": "2019-01-19T03:36:38.525111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "      <th>extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop  extra\n",
       "0       Mary   F    7065  1880  0.077643      0\n",
       "1       Anna   F    2604  1880  0.028618      0\n",
       "2       Emma   F    2003  1880  0.022013      0\n",
       "3  Elizabeth   F    1939  1880  0.021309      0\n",
       "4     Minnie   F    1746  1880  0.019188      0"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:37:47.580442Z",
     "start_time": "2019-01-19T03:37:47.263958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year  sex\n",
      "1880  F      38\n",
      "      M      14\n",
      "1881  F      38\n",
      "      M      14\n",
      "1882  F      38\n",
      "      M      15\n",
      "1883  F      39\n",
      "      M      15\n",
      "1884  F      39\n",
      "      M      16\n",
      "dtype: int64\n",
      "****************************************************************************************************\n",
      "sex    F   M\n",
      "year        \n",
      "1880  38  14\n",
      "1881  38  14\n",
      "1882  38  15\n",
      "1883  39  15\n",
      "1884  39  16\n",
      "1885  40  16\n",
      "1886  41  16\n",
      "1887  41  17\n",
      "1888  42  17\n",
      "1889  43  18\n"
     ]
    }
   ],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    \"\"\"https://stackoverflow.com/questions/21390035/python-pandas-groupby-object-apply-method-duplicates-first-group\"\"\"\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    r = group.prop.cumsum().values.searchsorted(q) + 1\n",
    "    return r\n",
    "\n",
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "print(diversity[0:10])\n",
    "diversity = diversity.unstack('sex')\n",
    "print('*' * 100)\n",
    "print(diversity[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:38:21.836965Z",
     "start_time": "2019-01-19T03:38:21.421444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15efc52d0>"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g/woSERERkSNUBYJ8b+5qUhJi+OUXx2F2YsMsaikkH0d8fDyrVq3yuwwREREROYbX1xSw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\nDRFxF/AsOTa1MmMzDkYZm7cC21JKW8u5h1JKfcZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1431c2190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The \"Last letter\" Revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:44:19.990382Z",
     "start_time": "2019-01-19T03:44:19.295064Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          y\n",
      "1          a\n",
      "2          a\n",
      "3          h\n",
      "4          e\n",
      "5          t\n",
      "6          a\n",
      "7          e\n",
      "8          a\n",
      "9          h\n",
      "10         e\n",
      "11         a\n",
      "12         a\n",
      "13         e\n",
      "14         a\n",
      "15         a\n",
      "16         a\n",
      "17         e\n",
      "18         e\n",
      "19         e\n",
      "20         e\n",
      "21         l\n",
      "22         e\n",
      "23         e\n",
      "24         e\n",
      "25         a\n",
      "26         e\n",
      "27         h\n",
      "28         e\n",
      "29         e\n",
      "          ..\n",
      "1690754    n\n",
      "1690755    n\n",
      "1690756    n\n",
      "1690757    n\n",
      "1690758    l\n",
      "1690759    n\n",
      "1690760    a\n",
      "1690761    o\n",
      "1690762    h\n",
      "1690763    i\n",
      "1690764    h\n",
      "1690765    n\n",
      "1690766    r\n",
      "1690767    n\n",
      "1690768    n\n",
      "1690769    n\n",
      "1690770    n\n",
      "1690771    i\n",
      "1690772    n\n",
      "1690773    b\n",
      "1690774    e\n",
      "1690775    t\n",
      "1690776    n\n",
      "1690777    r\n",
      "1690778    n\n",
      "1690779    e\n",
      "1690780    e\n",
      "1690781    s\n",
      "1690782    n\n",
      "1690783    x\n",
      "Name: last_letter, Length: 1690784, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# extract last letter from name column\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.name.map(get_last_letter)\n",
    "last_letters.name = 'last_letter'\n",
    "print(last_letters)\n",
    "table = names.pivot_table('births', index=last_letters,  # Keys to group by on the pivot table index.\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:45:23.144416Z",
     "start_time": "2019-01-19T03:45:23.018512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 26 entries, a to z\n",
      "Columns: 262 entries, (F, 1880) to (M, 2010)\n",
      "dtypes: float64(262)\n",
      "memory usage: 53.4+ KB\n"
     ]
    }
   ],
   "source": [
    "table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:45:23.524997Z",
     "start_time": "2019-01-19T03:45:23.397515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>31446.0</td>\n",
       "      <td>31581.0</td>\n",
       "      <td>36536.0</td>\n",
       "      <td>38330.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>45408.0</td>\n",
       "      <td>49100.0</td>\n",
       "      <td>48942.0</td>\n",
       "      <td>59442.0</td>\n",
       "      <td>58631.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39124.0</td>\n",
       "      <td>38815.0</td>\n",
       "      <td>37825.0</td>\n",
       "      <td>38650.0</td>\n",
       "      <td>36838.0</td>\n",
       "      <td>36156.0</td>\n",
       "      <td>34654.0</td>\n",
       "      <td>32901.0</td>\n",
       "      <td>31430.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50950.0</td>\n",
       "      <td>49284.0</td>\n",
       "      <td>48065.0</td>\n",
       "      <td>45914.0</td>\n",
       "      <td>43144.0</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>42123.0</td>\n",
       "      <td>39945.0</td>\n",
       "      <td>38862.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27113.0</td>\n",
       "      <td>27238.0</td>\n",
       "      <td>27697.0</td>\n",
       "      <td>26778.0</td>\n",
       "      <td>26078.0</td>\n",
       "      <td>26635.0</td>\n",
       "      <td>26864.0</td>\n",
       "      <td>25318.0</td>\n",
       "      <td>24048.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>609.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60838.0</td>\n",
       "      <td>55829.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>51754.0</td>\n",
       "      <td>50670.0</td>\n",
       "      <td>51410.0</td>\n",
       "      <td>50595.0</td>\n",
       "      <td>47910.0</td>\n",
       "      <td>46172.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>33378.0</td>\n",
       "      <td>34080.0</td>\n",
       "      <td>40399.0</td>\n",
       "      <td>41914.0</td>\n",
       "      <td>48089.0</td>\n",
       "      <td>49616.0</td>\n",
       "      <td>53884.0</td>\n",
       "      <td>54353.0</td>\n",
       "      <td>66750.0</td>\n",
       "      <td>66663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>145395.0</td>\n",
       "      <td>144651.0</td>\n",
       "      <td>144769.0</td>\n",
       "      <td>142098.0</td>\n",
       "      <td>141123.0</td>\n",
       "      <td>142999.0</td>\n",
       "      <td>143698.0</td>\n",
       "      <td>140966.0</td>\n",
       "      <td>135496.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>2195.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>2255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2151.0</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>2396.0</td>\n",
       "      <td>2666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>4863.0</td>\n",
       "      <td>4784.0</td>\n",
       "      <td>5567.0</td>\n",
       "      <td>5701.0</td>\n",
       "      <td>6602.0</td>\n",
       "      <td>6624.0</td>\n",
       "      <td>7146.0</td>\n",
       "      <td>7141.0</td>\n",
       "      <td>8630.0</td>\n",
       "      <td>8826.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85959.0</td>\n",
       "      <td>88085.0</td>\n",
       "      <td>88226.0</td>\n",
       "      <td>89620.0</td>\n",
       "      <td>92497.0</td>\n",
       "      <td>98477.0</td>\n",
       "      <td>99414.0</td>\n",
       "      <td>100250.0</td>\n",
       "      <td>99979.0</td>\n",
       "      <td>98090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>61.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20980.0</td>\n",
       "      <td>23610.0</td>\n",
       "      <td>26011.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>31317.0</td>\n",
       "      <td>33558.0</td>\n",
       "      <td>35231.0</td>\n",
       "      <td>38151.0</td>\n",
       "      <td>40912.0</td>\n",
       "      <td>42956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>1459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42477.0</td>\n",
       "      <td>42043.0</td>\n",
       "      <td>42296.0</td>\n",
       "      <td>41400.0</td>\n",
       "      <td>42151.0</td>\n",
       "      <td>42537.0</td>\n",
       "      <td>42136.0</td>\n",
       "      <td>39563.0</td>\n",
       "      <td>37507.0</td>\n",
       "      <td>35198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>2541.0</td>\n",
       "      <td>2911.0</td>\n",
       "      <td>3527.0</td>\n",
       "      <td>3848.0</td>\n",
       "      <td>4808.0</td>\n",
       "      <td>5144.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>6175.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>8395.0</td>\n",
       "      <td>...</td>\n",
       "      <td>153648.0</td>\n",
       "      <td>153493.0</td>\n",
       "      <td>153862.0</td>\n",
       "      <td>152800.0</td>\n",
       "      <td>155312.0</td>\n",
       "      <td>156234.0</td>\n",
       "      <td>155203.0</td>\n",
       "      <td>150791.0</td>\n",
       "      <td>143751.0</td>\n",
       "      <td>133583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41967.0</td>\n",
       "      <td>42663.0</td>\n",
       "      <td>42790.0</td>\n",
       "      <td>43054.0</td>\n",
       "      <td>41600.0</td>\n",
       "      <td>42503.0</td>\n",
       "      <td>43860.0</td>\n",
       "      <td>44316.0</td>\n",
       "      <td>46278.0</td>\n",
       "      <td>46808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>3008.0</td>\n",
       "      <td>2959.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>3837.0</td>\n",
       "      <td>4507.0</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>5242.0</td>\n",
       "      <td>5512.0</td>\n",
       "      <td>6833.0</td>\n",
       "      <td>7103.0</td>\n",
       "      <td>...</td>\n",
       "      <td>616099.0</td>\n",
       "      <td>630322.0</td>\n",
       "      <td>663419.0</td>\n",
       "      <td>676011.0</td>\n",
       "      <td>686326.0</td>\n",
       "      <td>720998.0</td>\n",
       "      <td>741355.0</td>\n",
       "      <td>733869.0</td>\n",
       "      <td>715388.0</td>\n",
       "      <td>688677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>30.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82146.0</td>\n",
       "      <td>83180.0</td>\n",
       "      <td>85423.0</td>\n",
       "      <td>88822.0</td>\n",
       "      <td>92001.0</td>\n",
       "      <td>96350.0</td>\n",
       "      <td>96895.0</td>\n",
       "      <td>91485.0</td>\n",
       "      <td>86423.0</td>\n",
       "      <td>81025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3419.0</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>2982.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>2739.0</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>2595.0</td>\n",
       "      <td>2409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>602.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>481.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>917.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165377.0</td>\n",
       "      <td>164821.0</td>\n",
       "      <td>169878.0</td>\n",
       "      <td>169452.0</td>\n",
       "      <td>172069.0</td>\n",
       "      <td>176490.0</td>\n",
       "      <td>177207.0</td>\n",
       "      <td>174632.0</td>\n",
       "      <td>173200.0</td>\n",
       "      <td>166064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>1391.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2524.0</td>\n",
       "      <td>2803.0</td>\n",
       "      <td>3582.0</td>\n",
       "      <td>3569.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143791.0</td>\n",
       "      <td>139595.0</td>\n",
       "      <td>138632.0</td>\n",
       "      <td>139642.0</td>\n",
       "      <td>139913.0</td>\n",
       "      <td>143232.0</td>\n",
       "      <td>142155.0</td>\n",
       "      <td>137056.0</td>\n",
       "      <td>129861.0</td>\n",
       "      <td>123670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>2152.0</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>2399.0</td>\n",
       "      <td>2554.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>2889.0</td>\n",
       "      <td>3017.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3816.0</td>\n",
       "      <td>3784.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47688.0</td>\n",
       "      <td>44991.0</td>\n",
       "      <td>43765.0</td>\n",
       "      <td>43870.0</td>\n",
       "      <td>43369.0</td>\n",
       "      <td>43553.0</td>\n",
       "      <td>43437.0</td>\n",
       "      <td>43846.0</td>\n",
       "      <td>43674.0</td>\n",
       "      <td>43398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>380.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>2052.0</td>\n",
       "      <td>2138.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>2417.0</td>\n",
       "      <td>2318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>2723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>52265.0</td>\n",
       "      <td>50103.0</td>\n",
       "      <td>49079.0</td>\n",
       "      <td>47556.0</td>\n",
       "      <td>45464.0</td>\n",
       "      <td>43217.0</td>\n",
       "      <td>40251.0</td>\n",
       "      <td>36937.0</td>\n",
       "      <td>33181.0</td>\n",
       "      <td>30656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10691.0</td>\n",
       "      <td>11009.0</td>\n",
       "      <td>11718.0</td>\n",
       "      <td>12399.0</td>\n",
       "      <td>13025.0</td>\n",
       "      <td>13992.0</td>\n",
       "      <td>14306.0</td>\n",
       "      <td>14834.0</td>\n",
       "      <td>16640.0</td>\n",
       "      <td>16352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>10469.0</td>\n",
       "      <td>10404.0</td>\n",
       "      <td>12145.0</td>\n",
       "      <td>12063.0</td>\n",
       "      <td>13917.0</td>\n",
       "      <td>13927.0</td>\n",
       "      <td>14936.0</td>\n",
       "      <td>14980.0</td>\n",
       "      <td>17931.0</td>\n",
       "      <td>17601.0</td>\n",
       "      <td>...</td>\n",
       "      <td>139109.0</td>\n",
       "      <td>134557.0</td>\n",
       "      <td>130569.0</td>\n",
       "      <td>128367.0</td>\n",
       "      <td>125190.0</td>\n",
       "      <td>123707.0</td>\n",
       "      <td>123397.0</td>\n",
       "      <td>122633.0</td>\n",
       "      <td>112922.0</td>\n",
       "      <td>110425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>106.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>2903.0</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>3473.0</td>\n",
       "      <td>3633.0</td>\n",
       "      <td>3476.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                F                                                        \\\n",
       "year            1880     1881     1882     1883     1884     1885     1886   \n",
       "last_letter                                                                  \n",
       "a            31446.0  31581.0  36536.0  38330.0  43680.0  45408.0  49100.0   \n",
       "b                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "c                NaN      NaN      5.0      5.0      NaN      NaN      NaN   \n",
       "d              609.0    607.0    734.0    810.0    916.0    862.0   1007.0   \n",
       "e            33378.0  34080.0  40399.0  41914.0  48089.0  49616.0  53884.0   \n",
       "f                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "g                7.0      5.0     12.0      8.0     24.0     11.0     18.0   \n",
       "h             4863.0   4784.0   5567.0   5701.0   6602.0   6624.0   7146.0   \n",
       "i               61.0     78.0     81.0     76.0     84.0     92.0     85.0   \n",
       "j                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "k               13.0     15.0     11.0     17.0     21.0     18.0     27.0   \n",
       "l             2541.0   2911.0   3527.0   3848.0   4808.0   5144.0   5721.0   \n",
       "m               58.0     57.0     81.0     86.0     79.0     75.0    103.0   \n",
       "n             3008.0   2959.0   3576.0   3837.0   4507.0   4735.0   5242.0   \n",
       "o               30.0     49.0     35.0     47.0     74.0     84.0     93.0   \n",
       "p                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "q                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "r              481.0    417.0    590.0    640.0    718.0    799.0    917.0   \n",
       "s             1391.0   1316.0   1637.0   1794.0   2039.0   2127.0   2524.0   \n",
       "t             2152.0   2165.0   2399.0   2554.0   2825.0   2889.0   3017.0   \n",
       "u              380.0    427.0    410.0    444.0    490.0    495.0    511.0   \n",
       "v                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "w                NaN      5.0      NaN      NaN      NaN      NaN      5.0   \n",
       "x                NaN      NaN      NaN      7.0      NaN      NaN      NaN   \n",
       "y            10469.0  10404.0  12145.0  12063.0  13917.0  13927.0  14936.0   \n",
       "z              106.0     95.0    106.0    141.0    148.0    150.0    202.0   \n",
       "\n",
       "sex                                       ...            M            \\\n",
       "year            1887     1888     1889    ...         2001      2002   \n",
       "last_letter                               ...                          \n",
       "a            48942.0  59442.0  58631.0    ...      39124.0   38815.0   \n",
       "b                NaN      NaN      NaN    ...      50950.0   49284.0   \n",
       "c                NaN      NaN      NaN    ...      27113.0   27238.0   \n",
       "d             1027.0   1298.0   1374.0    ...      60838.0   55829.0   \n",
       "e            54353.0  66750.0  66663.0    ...     145395.0  144651.0   \n",
       "f                NaN      NaN      NaN    ...       1758.0    1817.0   \n",
       "g               25.0     44.0     28.0    ...       2151.0    2084.0   \n",
       "h             7141.0   8630.0   8826.0    ...      85959.0   88085.0   \n",
       "i              105.0    141.0    134.0    ...      20980.0   23610.0   \n",
       "j                NaN      NaN      NaN    ...       1069.0    1088.0   \n",
       "k               19.0     21.0     22.0    ...      42477.0   42043.0   \n",
       "l             6175.0   7900.0   8395.0    ...     153648.0  153493.0   \n",
       "m               90.0    123.0    137.0    ...      41967.0   42663.0   \n",
       "n             5512.0   6833.0   7103.0    ...     616099.0  630322.0   \n",
       "o               97.0    134.0    142.0    ...      82146.0   83180.0   \n",
       "p                NaN      NaN      NaN    ...       3419.0    3157.0   \n",
       "q                NaN      NaN      NaN    ...        602.0     618.0   \n",
       "r              910.0   1207.0   1214.0    ...     165377.0  164821.0   \n",
       "s             2803.0   3582.0   3569.0    ...     143791.0  139595.0   \n",
       "t             3140.0   3816.0   3784.0    ...      47688.0   44991.0   \n",
       "u              476.0    541.0    469.0    ...       1833.0    1819.0   \n",
       "v                NaN      NaN      NaN    ...       1209.0    1332.0   \n",
       "w                NaN      NaN      NaN    ...      52265.0   50103.0   \n",
       "x                NaN      NaN      NaN    ...      10691.0   11009.0   \n",
       "y            14980.0  17931.0  17601.0    ...     139109.0  134557.0   \n",
       "z              188.0    238.0    277.0    ...       2840.0    2737.0   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2003      2004      2005      2006      2007      2008   \n",
       "last_letter                                                               \n",
       "a             37825.0   38650.0   36838.0   36156.0   34654.0   32901.0   \n",
       "b             48065.0   45914.0   43144.0   42600.0   42123.0   39945.0   \n",
       "c             27697.0   26778.0   26078.0   26635.0   26864.0   25318.0   \n",
       "d             53391.0   51754.0   50670.0   51410.0   50595.0   47910.0   \n",
       "e            144769.0  142098.0  141123.0  142999.0  143698.0  140966.0   \n",
       "f              1819.0    1904.0    1985.0    1968.0    2090.0    2195.0   \n",
       "g              2009.0    1837.0    1882.0    1929.0    2040.0    2059.0   \n",
       "h             88226.0   89620.0   92497.0   98477.0   99414.0  100250.0   \n",
       "i             26011.0   28500.0   31317.0   33558.0   35231.0   38151.0   \n",
       "j              1203.0    1094.0    1291.0    1241.0    1254.0    1381.0   \n",
       "k             42296.0   41400.0   42151.0   42537.0   42136.0   39563.0   \n",
       "l            153862.0  152800.0  155312.0  156234.0  155203.0  150791.0   \n",
       "m             42790.0   43054.0   41600.0   42503.0   43860.0   44316.0   \n",
       "n            663419.0  676011.0  686326.0  720998.0  741355.0  733869.0   \n",
       "o             85423.0   88822.0   92001.0   96350.0   96895.0   91485.0   \n",
       "p              2982.0    2841.0    2768.0    2721.0    2739.0    2637.0   \n",
       "q               585.0     523.0     446.0     430.0     431.0     339.0   \n",
       "r            169878.0  169452.0  172069.0  176490.0  177207.0  174632.0   \n",
       "s            138632.0  139642.0  139913.0  143232.0  142155.0  137056.0   \n",
       "t             43765.0   43870.0   43369.0   43553.0   43437.0   43846.0   \n",
       "u              2052.0    2138.0    2129.0    2201.0    2311.0    2405.0   \n",
       "v              1652.0    1823.0    1794.0    2010.0    2295.0    2418.0   \n",
       "w             49079.0   47556.0   45464.0   43217.0   40251.0   36937.0   \n",
       "x             11718.0   12399.0   13025.0   13992.0   14306.0   14834.0   \n",
       "y            130569.0  128367.0  125190.0  123707.0  123397.0  122633.0   \n",
       "z              2722.0    2710.0    2903.0    3086.0    3301.0    3473.0   \n",
       "\n",
       "sex                              \n",
       "year             2009      2010  \n",
       "last_letter                      \n",
       "a             31430.0   28438.0  \n",
       "b             38862.0   38859.0  \n",
       "c             24048.0   23125.0  \n",
       "d             46172.0   44398.0  \n",
       "e            135496.0  129012.0  \n",
       "f              2212.0    2255.0  \n",
       "g              2396.0    2666.0  \n",
       "h             99979.0   98090.0  \n",
       "i             40912.0   42956.0  \n",
       "j              1416.0    1459.0  \n",
       "k             37507.0   35198.0  \n",
       "l            143751.0  133583.0  \n",
       "m             46278.0   46808.0  \n",
       "n            715388.0  688677.0  \n",
       "o             86423.0   81025.0  \n",
       "p              2595.0    2409.0  \n",
       "q               377.0     342.0  \n",
       "r            173200.0  166064.0  \n",
       "s            129861.0  123670.0  \n",
       "t             43674.0   43398.0  \n",
       "u              2417.0    2318.0  \n",
       "v              2589.0    2723.0  \n",
       "w             33181.0   30656.0  \n",
       "x             16640.0   16352.0  \n",
       "y            112922.0  110425.0  \n",
       "z              3633.0    3476.0  \n",
       "\n",
       "[26 rows x 262 columns]"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:47:02.750179Z",
     "start_time": "2019-01-19T03:47:02.693879Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
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     "execution_count": 468,
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   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:47:18.806895Z",
     "start_time": "2019-01-19T03:47:18.724791Z"
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       "      <td>649.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>609.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4511.0</td>\n",
       "      <td>4082.0</td>\n",
       "      <td>3764.0</td>\n",
       "      <td>3763.0</td>\n",
       "      <td>3565.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>33378.0</td>\n",
       "      <td>34080.0</td>\n",
       "      <td>40399.0</td>\n",
       "      <td>41914.0</td>\n",
       "      <td>48089.0</td>\n",
       "      <td>49616.0</td>\n",
       "      <td>53884.0</td>\n",
       "      <td>54353.0</td>\n",
       "      <td>66750.0</td>\n",
       "      <td>66663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>316007.0</td>\n",
       "      <td>315736.0</td>\n",
       "      <td>322199.0</td>\n",
       "      <td>323747.0</td>\n",
       "      <td>322305.0</td>\n",
       "      <td>330236.0</td>\n",
       "      <td>334422.0</td>\n",
       "      <td>332752.0</td>\n",
       "      <td>322682.0</td>\n",
       "      <td>313833.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                F                                                        \\\n",
       "year            1880     1881     1882     1883     1884     1885     1886   \n",
       "last_letter                                                                  \n",
       "a            31446.0  31581.0  36536.0  38330.0  43680.0  45408.0  49100.0   \n",
       "b                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "c                NaN      NaN      5.0      5.0      NaN      NaN      NaN   \n",
       "d              609.0    607.0    734.0    810.0    916.0    862.0   1007.0   \n",
       "e            33378.0  34080.0  40399.0  41914.0  48089.0  49616.0  53884.0   \n",
       "\n",
       "sex                                       ...                         \\\n",
       "year            1887     1888     1889    ...         2001      2002   \n",
       "last_letter                               ...                          \n",
       "a            48942.0  59442.0  58631.0    ...     673418.0  681001.0   \n",
       "b                NaN      NaN      NaN    ...        394.0     332.0   \n",
       "c                NaN      NaN      NaN    ...        539.0     607.0   \n",
       "d             1027.0   1298.0   1374.0    ...       4511.0    4082.0   \n",
       "e            54353.0  66750.0  66663.0    ...     316007.0  315736.0   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2003      2004      2005      2006      2007      2008   \n",
       "last_letter                                                               \n",
       "a            702628.0  710441.0  727357.0  753391.0  752779.0  723405.0   \n",
       "b               344.0     601.0     369.0     367.0     409.0     408.0   \n",
       "c               649.0     663.0     613.0     633.0     775.0     926.0   \n",
       "d              3764.0    3763.0    3565.0    3600.0    3355.0    3239.0   \n",
       "e            322199.0  323747.0  322305.0  330236.0  334422.0  332752.0   \n",
       "\n",
       "sex                              \n",
       "year             2009      2010  \n",
       "last_letter                      \n",
       "a            698477.0  670605.0  \n",
       "b               435.0     450.0  \n",
       "c               931.0     946.0  \n",
       "d              2864.0    2607.0  \n",
       "e            322682.0  313833.0  \n",
       "\n",
       "[5 rows x 131 columns]"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtablex = table.reindex(columns=['F'], level='sex')\n",
    "subtablex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:47:34.460072Z",
     "start_time": "2019-01-19T03:47:34.430110Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:48:48.503524Z",
     "start_time": "2019-01-19T03:48:48.475357Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "letter_prop = subtable / subtable.sum().astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:48:53.693156Z",
     "start_time": "2019-01-19T03:48:53.611753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:56:50.604975Z",
     "start_time": "2019-01-19T03:56:50.560190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 26 entries, a to z\n",
      "Data columns (total 6 columns):\n",
      "(F, 1910)    21 non-null float64\n",
      "(F, 1960)    24 non-null float64\n",
      "(F, 2010)    26 non-null float64\n",
      "(M, 1910)    24 non-null float64\n",
      "(M, 1960)    24 non-null float64\n",
      "(M, 2010)    26 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 2.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 26 entries, a to z\n",
      "Data columns (total 3 columns):\n",
      "1910    24 non-null float64\n",
      "1960    24 non-null float64\n",
      "2010    26 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "letter_prop.info()\n",
    "letter_prop['M'].info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:56:57.596614Z",
     "start_time": "2019-01-19T03:56:57.558276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year             1910      1960      2010\n",
       "last_letter                              \n",
       "a            0.005031  0.002440  0.014980\n",
       "b            0.002116  0.001834  0.020470\n",
       "c            0.002482  0.007257  0.012181\n",
       "d            0.113858  0.122908  0.023387\n",
       "e            0.147556  0.083853  0.067959"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop['M'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:58:51.406271Z",
     "start_time": "2019-01-19T03:58:50.366355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17298e910>"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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N/OhHP2L+/Pn87Gc/46CDDgLgZS97GT/72c+et+/g4CDTp0/fVp4+fTqDg4M9j9GkS5Ik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\nQJKuiIiIiB5I0hURERHRA/8Ff7DF9nVn2RkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x171c6fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female',\n",
    "                      legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T03:58:55.776931Z",
     "start_time": "2019-01-19T03:58:55.746291Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x171432250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots_adjust(hspace=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:04:01.106743Z",
     "start_time": "2019-01-19T04:04:01.058125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table / table.sum().astype(float)\n",
    "\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:04:01.986968Z",
     "start_time": "2019-01-19T04:04:01.953622Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:04:04.947819Z",
     "start_time": "2019-01-19T04:04:04.595866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x175bb9e90>"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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we/UuXv9uJ44Dg7pGMbZ3LON6xzEsKZpAf01vL0fg9UDeKtj8GWz+FHYuBeOB\noCiY9BcYdb1O7hNpYRzTcKilhUhPTzfLli3zdRki0sJ5vIb1eaUsyypmaVYRy7KKyC+tAWBItw6c\nNbATkwd0plvHph2pq/N4+T67hK83FbBw0x5W7NyLx2sID/LnspO6c+24Hhp5lgNqKyEnA3YsgR3f\n2IBcW2Yf6zwYek208ysnpmvKOJETzHGcDGNM+hG3U2AWkdYkv7SaLzbsZsH6PSzaUkBZdR0AnaOC\nGZHckREpHTktNZ4uHUJOWE2l1W6WbCnkg+9zmb16F6EBLq4ck8LV41LoEKrg3G55PXbBkYV/A08t\n4EB8GiSNtpcep0J4vI+LFGnfFJhFpE3weg0rdu7ls3X5LNiwh3W7bJtF56hgTukTx6geHRmR3JHE\n6JbR77kxv4wnPt3E7NW7iAjy5/KTk+nXOZIAl0Ogvx+B/n4E+fuR2imSsCB1xbVZZfnw7tWw7Svo\nPx0GXwLdRkBItK8rE5EGFJhFpNVye7ws2VrIvLV5zF+bz+6yGlx+Dundozm1bzwTUuPomxDRomer\nWJ9XyhOfbuLjNXmHfDw00MWUQZ25cEQ3hiVFt+j3Ikdp65d2WriaMjjrERg6Qz3JIi2UArOItDrb\nCir4z9db+WBlLqXVdYQEuDi1bxxn9u/EhNR4okJaX39nfmk1eyvduD1eauq8uD1eyqvrmJ+Zx0er\ndlFZ66FnXBgXpHdjYGIUbo/BXeel1mO3jYsIYlRKDC4/Ba4Wz+uBL/8KXz4Msb3h/JmQkObrqkTk\nMBSYRaTVyNhezL+/2sq8zDwC/PyYMqgzkwd2Zlzv2EbNYNFaldfUMXtVLm8uyyZje/FPbhcbHsSU\nQZ05Z3BnjUa3VHt3wKzrYfsi235x9t8gMMzXVYnIESgwi0iL5vZ4+SQzn/98vY2M7cVEhQRw2eju\n/PLk7sRHBPu6vBNu655y8ktrCPR3CHS5CPT3I8DlsCGvjA++z+Wz9buprfPStUMIPx/WletO6ake\n6JbAGFj9Fsy+2X591iMw5GJfVyUijaTALCIt0vbCCl7/bidvLcumoLyGxOgQrh6bwgUjumkp6sMo\nq3bzSWY+H3yfyxcb9tA5Kph7p/bnjLQEjTj7SlWxDcpr3oFuo+G8ZyA62ddVichRUGAWkRbls3X5\n/HdRFl9vLsDl5/Cz1HguGZnE+D5x6s89Shnbi7hr1hrW55Vxer947p3af/8sIR6vITO3lG+3FbJl\nTzkXj0xvPWc8AAAgAElEQVRiUGIHH1fc/Iwx5Ffmk1mYyfbS7VTVVVFdV22vPdXUemoJ8AsgyBVE\noCuQIFfQ/q+D/YP33xfostMAerwePMaDx+uhztQxMHYgqR1T7Yt53LB+Nsy7C8rz4NQ7YOzvwa/t\ntg+JtFUKzCLSItTWebl/diYvfrOdrh1CuGhEN85P70anqPbXdtGU3B4vLyzK4rFPN2IMXDiiG1mF\nFWRkFVNWY+emDvL3w+3xcvW4Hvz+9D6EBLadQOf2usnIz2DprqVkFmayrmgdRdVFP9gm2BVMsL+9\nBPoF4va6qfHUUOuppcZTg9vrbvTrOThMTZzAje4g4lbPgordENPbjip3Hd7Ub09EThAFZhHxuV0l\nVfzmleWs2LGXa8f34H/O7Iu/S0tHN6WcvVXc+8FaPsnMp1d8OCNTOjIqpSOje8QQHODiLx+v57Wl\nO+geE8pD0wdycq9YX5d8zKrrqlmcu5jPdnzGFzu/oLS2FJfjomeHnqTFpNGvYz/SYtLo1aEXoQGh\n+DmH/1nzGu8PAnSNp4aauhocx8HluHD5ufCvc+PdNJ83Ml/kJUoJNIZrAjpz2fAbCeozGVxqIxJp\nzRSYRcSnFm8p4P+9toKqWg+PnD+YswZ29nVJbVpNnYcg/0OPIH+zpZA73l1FVmElF6Z3486z+7Xo\nKfo8Xg+7KnaRVZrF9tLtbCvZRlZpFqv2rKKqroqIwAhOTTyV05JO46QuJxEa0AyL1uz6Hpa/CKve\ngpoSiE5hx8Bz+VvdLhbsWkzX8K78YfgfmNh9onrIRVoxBWYR8YnymjpmLs7i7/M30CMunH/NGE6v\n+HBfl9XuVbs9PPbpRp5buI2EiCAeOX8wY1rQaHOlu5JFuYtYsGMBX+V8RUlNyf7HIgIiSI5KJi0m\njZ8l/YwRnUYQ4NcMgb+mDFa9Cctn2sDsCoK0aTDsl9B9DPjZEeslu5bw8NKH2bx3M/069uN3Q3/H\n2K5jGx2cvcZLnbduf7+0iPiOArOInDB1Hi8LNxcwa3kO8zPzqHZ7OXtgZx7+xSDCNfVZi7Jy517+\n8MZKthZUcOWYZG6blOqzua69xstHWz9i7ra5fLvrW2q9tUQFRXFK4ikMjR9KcmQyyVHJxATHNO8o\n7u718N1z8P3rUFsGCQNg2OUw6PyfXMra4/Uwe9tsnl75NDnlOQyLH8bvhv6O9E7pVNdVs7F4I+uL\n1rOuaB3bSrZRWltKeW25vbjLAZjWaxo3DruR2JCW88FFpL1p0sDsOM4k4AnABTxnjPnLQY+nAv8F\nhgF3GWP+1uCxLKAM8AB1jSkqvXu4WXbbwAYv4Ad9zoSTf/eTv7xEfMZdDdnfQfZSCOlo/9jG94Og\nnxhVra20Z9P7BzVPPV4vlOXahRTi+zXr/5ldJVU8t3Ab76/MpaC8hqiQAKYM6sz0oV0Z3l0LbLRU\nVbUe/vLxOmZ+s52ecWE8duGQEz6Txs7Sndy96G6W715OYngiE5ImMKHbBIbGD8Xf7wR8yNo308V3\nz0HWQnAFQv/pMOIaSExv9FLWbo+bWZtn8cz3z7C7ajddw7uSV5GHx3gAiAiMoHeH3kQFRRERGEF4\nQDjhgeGU1JTwzqZ3CHIFcd2g65jRbwYBrpbbJiPSVjVZYHYcxwVsBCYC2cB3wMXGmMwG28QD3YFz\ngeJDBOZ0Y0xBY4tP7xljlj04+cAdNWWw9QsIioKTb4BR10Nw5IHH62rs6kqbPoG6akg6GZLHQGSX\nxr5k41WXwMZ5ULjFTidUlm+vy3dD95Ph7L9DcFTTv674njFQWwEVe6A0B7Yvhm1f2bBcV/3j7aOT\nIb6/Dcflu+1Z9eW7obYccKBDkl0+N7YPxPSC8AT7PO5KG8LrquxSuyHRENoRQmPsJTAcqopsHeV7\nDjxvcZb9uSzedqCegFAYOgNG/xo69miyb0VRRS1PL9jMi0u2Y4zhtNQEzh3alQmpcT/ZRystz8JN\ne7j1rVUUlNdwer8EhiR1YHBiBwYmRjXbkQGv8fLGhjd4LOMxXI6L20bexrSe007ch6uyPMiYCRn/\nhbJdEJUE6VfatouwYx/pra6r5o0Nb7By90p6duhJv479SI1JpUtYl598b1klWTyy7BG+yv6K7pHd\nuTX9VsYnjtcHTZETqCkD80nAvcaYM+tv3wFgjHnoENveC5Qfd2A+VEtG3hpY8CBsmG1H8cbeZK83\nzrVhurbc9pu5Au0hNYDoFBuce02Efuccfo5MrwfyVkNIB4joAv4NestqK+3rrHkHNs0HT629PzQW\nIjrZoBMcCZkf2JB04cuQkNbYtystkccNO76x/947lkB5vg2ndVUNNnKg0wBIHg8p4yDpJPuBKn9t\n/WUN7M6024XH25+T8HgIi7Mf8go3QcFGG3LdlcdXr3+w/dnr2OPAJbKL/Zlc/RYYD6SeDSf9DpJG\n/Xh/d7X9EFCaW3+dY+vuexbEp+7frLymjv8s3Ma/F26lsraO84YlctPpvffPASytT0mlm0fmr+er\njQXsKLI/h44DveLCOaVPHJed1J3uMU2zxHNOeQ73LLqHpXlLGdNlDPeefC+dwjo1yXMfltcLOxbb\n0eR1H4K3DnqeBiOvgd5n+Hz+5IXZC/nrd38lqzSLIXFDuGLAFUzoNuGIs3yIyPFrysD8C2CSMebq\n+tuXAaOMMTccYtt7+XFg3gaUYFsynjHGPPsTr3MtcC1AUlLS8O3btx+6oJwM+PwB2PKZvR3Z1f7C\n6zMJUsbbwJy/2o7+ZS2yI8/Ve+0o3im32UNuDX85uqvh+9dg8ZNQtGVfNTYIR3a1o3vbF4O7wgae\n/ufBgPOgy1A4+PDZ9sXw1hV2RPycf9j+t4Pt3QHVpTZoSctSUw7rPrAfjrYsgJpS8AuAbiMhqpsd\nfQqLqw+/8dBlmB35PV77WigqC+2IsH8wBITYi+NnVxOrLKy/FNmfr5DoA+E7LA6CIn76EHLpLlj6\nLCx73v5fONSJRvs+BB5KbB+qe0/h/dp0/roigMJKN5P6d+LmM/rQOyHi+N+/tBhFFbV8n72XVTtL\nWLGzmK83FeAxhgl947n85GTG9YrF7xgWmamqq+KlzJd4bvVz+Dl+3Jp+K+f1Pq/xI6l5q+G7/9jf\nn+EJEJEA4Z3sdWQixPb6ceuR12uP/mS+B5nv2w+BwVEw9DJIvwpieh71+2hObo+btze9zcy1M8kp\nzyE5Mpkr+l/BlJ5TCHI1U/uWiLSowNzVGJNT37bxCfA7Y8xXh3vNRp30t+t7GyYSBhy+18zrtSHo\ny4ftaF9sXzjlf6DXafaw3JKn7ehh5yEw8lowXijJrr/stIe6u42EAT+H5LFHHokoy7Ohecc39vnG\n32q/3vqFvRRttdt1H2MDfMr4RvfKSTPasQRmXWfbGsI7Qe+Jtm++x6k2jLYFtRWw6g0bOg4WEAZR\nXe2odGQiRHaGmjL2Ln+XvcveplvpclyOId+/M/SbSsKoC6HrMP3stnH5pdW88u0OXv12BwXlNfSI\nDePCEd0Y1zuO1E4RRwzP+07q+8fyf5Bfmc+EbhO4feTtdAlvRLucx21/dy/9t/0d6h8CcX3rW5Hy\n7ShxQ2FxdiGR2N72Q+GGOTYkuwLtaHL/c6HfVAhs2UdD6rx1fLr9U55f8zzritYRGxLLpf0u5fw+\n5xMVpHY/kabWYloyjubxfZpllgyvF9a9D188DHvW2bBtvNBjgm3vSDml6f74e9zwyf/CkqcO3BcY\nbgN3j1Nt+8fiJ23vc9JJNjj3OFXhwxfqauGLh2DR4xCVCFOfbNqfhVaooqaO5TuKmbUihw9W5mKA\ni/uH8pvOG+iSO99+8PPW2WDd7xxImwqJI7WAQxtWU+fh49V5vLA4i5U79wIQExbIST1jGNMrloFd\no6jzGqrdHqrcHqpr61hfsoJP8p5jZ8VmUqPTuHXELYzsPOLQL2CMDcGFm+1lzwZY8679Hdmhu22d\nGDrjwCiy12uPvJTn2Q+ABfvamzbb65oy6HU6pJ0LfSe1yvNKjDF8m/ctL6x5gUW5iwj1D+UXfX7B\nZWmXnZg2FpF2oikDsz/2pL/TgBzsSX+XGGPWHmLbe2kQiB3HCQP8jDFl9V9/AvzJGDP3cK/ZrNPK\neb32EN3OpTD4Qtta0Vw2zoO8VZA8zi6d2rCFw11tJ8X/+jF7OL5rOgy60AaQSC3w0GSq9tpWG4Md\nPQ2PP3CUID8TZl1rD/cOvQwmPdR2RpOPQnlNHUu3FfLt1iK+3VbEmpwS6ryG0EAXF41I4qqxyT/s\nUa4qhg1z7ejf5s/AU2NPyE0ZZz/49ZhgD3e34w8dbdmukioWbS5k8eYCvt5cwO6yGnDq8AvOwRWS\nhSt0O66Q7fj5V+Ct7UDNnknUlQ7Cz/EjNjyIiWkJXDwyiQGxLnteyPev2f+DteUHXsQVaI++jbzW\nBt+j7TH2enzel9yUNhRt4IW1L/Dxto9xcJicMpmrB15Njw5NdyKvSHvV1NPKnQU8jp1W7nljzAOO\n41wPYIz5l+M4nYBlQCTgBcqBNCAWmFX/NP7Aq8aYB470eu1qHua6GljxMnz7DBRssPcljrSjdqln\n2zO4DzVyV1lkTyzbnQl71ttZFgZdeFxneZ9QZfl2KqeSnfU9iZ0gorO9Du5w+LBVvhu2fmkP03pq\n7R9X//oTPl0Bti1m30hVxZ4f7uvnb18nsgvkrrQBeeo/7Pe6HfF4DYu3FPBORjZz19p5kwNdfgzu\nFsWolBhGpnRkePdowo40U0JNGWz+1PZ8b11woN0jqpsNzz0nQMqpEBbTzO9ITrTCqkI+3/E5H26e\nz+rCDOqMG4BOgbEMDIhjiBPBiIgRFAUms8PpzK6aYLYVVrJj7RLO5xPOC1hMqKnCE5uKq+ep9ndY\nxx72OiqxTQXeppJbnstLmS/xzqZ3cHvd/Hrwr7lqwFUnZho+kTZKC5e0Rns22FkN1r1vR1z2CQy3\nhxSDO0BgmA0l5XkHHg+KrD9BzR/6TrajpT1POzGHyAs21R+e7/LThz3dVTbE5q2207BlLbQh/6cE\nhB6Y8WHfdWhH2PmdbQfYXX9wIyjSfj88tba1wlNrRzvD4u0f3dhe9jqmFzguKM2GkgYzQUR2hTPu\nh/C4Jv6mtEwer2HdrlJmr97Feyty2FVSTWSwP1MGd2HKoM4MS4o+vgUsjLE9+lsX2AC9baFdUhgH\nOg+yI8+9Toek0T8+YbapuKvt7CQJAyAguHleox3bU7mHuVlz+XT7p6zYvQKDIdEvlAkeP4aXFDK4\nZA+xXu+hdw6JtjMbFW2hzi+IBa4x/LN8POsDUjkjrRNnDezM+D5xPltEpTUpqi7iwW8fZF7WPNJi\n0rh/zP30ju7t67JEWiUF5tauaCts+RwqCu1UZdUldoaDmlLbO5qQBvH1l4hONoCueNmuVFVZYEdR\nU8bbP1ChHev/WDW47LsvKLK+fzDPvua+S1WxXRI25dT9y8H+wO718Nmf7DR/+wRG2OAc1dWG6H1z\nVFcfWOKWgDDofpJtU0kZZ0/SqdhjA3V5nr0uybFzCRdttSfh7ZtT2BVk9+1xqr10GvTjUShj1ApQ\nr6C8hpU79rJ8RzErduzl++y9VNZ6cPk5jO8dy8+HJ3J6v4TmCyieOshdcSBAZy+1PxdBUXbkuc8k\ne3Ll8R4VcVfZUe7M922rSG2Zna96+JUw4lfNMx97e2IMa7bO4+W1LzKveA11GHq76zi9vILTKirp\nQwBOwgB7Ql5snwNzi0d0tidPF22pP+KzxX5Y7TkBBl2ICYlmxc69vLF0J3PX5lFS5SYs0MXP+iVw\n1oBOTEiNV3g+gvlZ83ng2wcoqy3j14N/zZUDrtRos8hRUmBur+pqYdM8WPGKHYmtLD4wL/WhOC47\nMu2pOXCfn7+d2qy23I7OjrgahlxiR5BLsmHBQ/D9qzb8nvw726/acA7fkhw7griv1SI8wf7xjOll\nZ1Y4mtFFr9cuLlCeb1etCwg59u9NG+b2eFm3q5QVO/ayYkcxy3fs3T+nrr+fQ1qXSIZ268DQpGhO\n7hVDfIQPRl/3LUC0cZ6d37o8n/0LuITF2nnNQ2Ns+0ZoTIPb9df+wXafsrwDPxN71sOmT+20jyHR\nkDrFzkCz7kM7S4Kfy86MMOp6O9tNO/4wZYxhwc4FPLf6OQqqCqj11FLrraXWU4vb6yYuJI7Ujqn0\nie5DamBH+uZtYF3OIl6p3cXKQH/CvF6ml1dxQUgSKV1G2PM/ugy14fg42yfcHi/fbCnk4zW7mLc2\nn6KKWpJjQvn7BYMZ3r0Jpm5sw4qqi3hgyQPM3z6fPtF9uGbQNUxMmohLLS0ijaLALAd43HbEeN+l\nsqj+6/rrupofLnoR1c0udLH2Pfju33Yu04Aw6HGKPckLY5ePHXezelN9KHdvFZ+v380XG3azaHMh\nVW67FG98RBDDkqIZ1t0G5AFdoggJbGF/PL1eyPvers5ZsMkeFakosD+blQWHXjnxRxw7etz7DHs0\nJHnsDz+MFW2zC1Usf8m2hnTobke1+5wB3ce2q5aNNQVr+Nuyv5GRn0FKVAqDYgcR6Aq0F79A/P38\nyS3JYkP+crZVF+Jp8Lmim18Il8SP5ty0SwjvMrz52mnq1Xm8fLlxD/e8v5ZdJVVcM74Hvz+9j0ab\nj2B+1nyeXPEkWaVZdI/szlUDruKcHudouW2RI1BglqaTu8IGj/VzbOCYcIcdFZQTprTaTVZBBdsK\nKsjcVcqXG/awPs8eOUiMDmFC33hG9ejIsKRoOkcFt+6ldfctQV5ZWB+k6xdtqauqX6yi/hIW37g+\n/ZpyOxvDhjn2ZNG6qvoPgKfaoxaBYfY8gcAwCAq3P9sJA9vENHm55bk8sfwJ5mybQ8fgjvx2yG85\nr/d5Bw7bl+6yR6Q2fGw/DHvd1MSlsjn1dDbG9iAmpjdjuozxyWhleU0dD8zO5LWlO+mTEM6jFwxh\nQNfWNz3cieTxevhsx2c8t/o51hWtIz40nqsGXMXFqRdr1UCRn6DALNJK7DsZb9PuMooq3BRX1FJc\naS+7S2vIKqygoPzASnz+fg4jkjsyITWOn6XG0zMuvHUH5BPJXWVPRtw4FzZ/YluMzCFOUguKtCcn\ndh9j++07D2r2kdWm5Pa6mbl2Jv9c+U8cx+GXab/kqgFXER4QBrtW2l7vjR/bBaDAzsaTNtXOtNNp\nYItqXVmwYTe3v7OKwvJarh7Xg+vG9yA67BCrVcp+xhi+yf2GZ1c/S0Z+BuMTx/PQuIeIDIz0dWki\nLY4Cs0gLUFlbR1FFLS4/Bz/HXlx+DtsLK/h2WxHfbi1kWVYxZTUHVi3zc6BDaCDRoQHEhgeRHBNG\nSlwYKbH2ktQxVIenm4oxtiWpttxeasptX3TW17B9kV0EY5/AiPrZaiLrr6NssG54X2C4DeCeWtsK\n5XHb2x262VVG4/o0/VzfdbV2MaaSbIjpzXrc3LPkPtYVrWNi94n8z4j/oVPFXljzth1pL9pqF25K\nHGlXs+wzyY60t6CQfLCSSjf3fbSWWStyCAlwcdno7lw9rgdxEVoy+nCMMbyx4Q0eXvowXcK78PiE\nxzWbhshBFJhFfGzxlgJ++8pyiivdP7lNz7gwRvWIYVRKRwZ0jSImLJDI4IAjLjksJ0hZvg3OezbY\nGWr2z1hTctDtUtv33xiRXe2JclGJ9oTY8AQ7tWF4gj0Jt6bM9lzXlNmLx91grvEA+3VNue0B3/W9\nnbHG66YW+Fd0FP+NiiQKP+4K7cvEmIGwcT7kr7YhOWU8DPgF9D2rVZ5/sCm/jKcWbOaD73MJcPlx\n8cgkrjulB52jdDLw4azYvYI/fPEHKtwV/GnMn5iUPMnXJYm0GArMIj708pLt3PvBWpJjw7h6bAoG\n8BqD12vweA3xkcGMTOlIbLhGyNqEfX3XteV2lhk/fxtu/QLsyG3xdjtyXbAB9my016W7oGL3oVtC\nGiM0BjoPhk6DWBIWwUM589lauYupwYn8T00AUbvX25lEEkfYkNx/OkQkNO379pFtBRX884vNvLs8\nB8eBcwZ14aqxKepxPozdlbu5+YubWblnJVf0v4Ibht5AkEu/f0QUmEV8wO3x8qcPM3lpyXYm9I3j\nHxcPJSK49fS+ygnm9diZQcrz66fZw7Z5BEUcuLgC6ts7ag9cXEEQ0Yn1xRt4LOMxFucupktYF+4e\nfTfjEscdeH53dZueDWRnUSXPL9rGm9/tpKLWw0k9Yrh6XAoT+sbrKM0huD1uHv7uYd7Y8Aadwzpz\nw9AbODvlbE1BJ+2aArPICba3spbfvLKcxVsKuXZ8D26blIpLf7SlGeSU5/DkiieZvXU2UUFRXDPw\nGi5KvajdjhiWVLl547sd/HdRFrtKqukZF8ZNp/fh7IGdFZwP4dtd3/JoxqNkFmbSJ7oPvx/+e8Z0\nGXPUJw9XuitZV7SONQVrKKstIyYkhpjgmP3XncM7t9ufSWk9FJhFmogxhrzSalZll7Amp4TVOSVs\nyCujzmvwc8DlODiOQ1m1m2q3lwfPG8gvhif6umxpg9YWruXNDW/y4ZYP8XP8mNFvBlcNvEqzH9Rz\ne7zMWb2LpxdsYUN+Gf06R3LLGX34WWq8ZpI5iNd4mbttLv9Y8Q9yynMY0WkEaR3TCPIPItgVTJAr\niGB/e3TCYzx4jZc6bx113jp2lu1kdcFqNu/djLe+pcjBwfDDPBEdFM1fT/krozuPPuHvT6SxFJhF\nmsD7K3N4YPY6dpfZlRBdfg6948NJ6xxJUIALY4ztTTbgABePSmJYUrRvi5Y2paquirnb5vLmhjdZ\nU7iGEP8QpvSYwnWDriMhrG30JDc1j9fw0apcHv1kI9sLKxma1IFbzujLST1iNOJ8kFpPLW9tfIsX\n175IcU0x1XXVPwq+B4sKimJAzAAGxA5gYOxA+sf2JzoomuKaYgqrCimsKqSguoDnVz9PVmkWN6ff\nzIx+M/ShRVokBWaR4+D1Gv42fwNPf7GFYUkdmDakKwO6RpHWObLlrZonrYYxhsW5i3l749v4+/nT\nt2NfUjumktoxldiQWIwx5Ffms7F4I5uKN7GxeCMLcxZSVltGj6geXND3Aqb2nEpEYBNPTddGuT1e\n3lqWzT8+20ReaTUxYYGM7R3LuN5xjOsdS0Jk2+3vPlbGGNxeN9WeaqrrqnFw8HP88Pfzx8/xw+W4\nCPEPaVT4rXBXcOfCO/l85+dM7TmVP47+4/5Ra5GWQoFZ5BiV19Tx+zdW8klmPheP7MZ9UwcQ6K9V\nsuTY1Xpqmb11Ni9mvsjmvZuJCY4hyBVEbkXu/m06BnfE7XVTVlu2/75OYZ0YGj+U8/ucT3pCukbo\njlG128Oc1bv4auMevt5csH8hoD4J4Qzp1oH+XaJI6xJJv86RhAe1/hUeWxKv8fLMqmd4euXT9I/p\nz+MTHqdTWCdflyWynwKzyDHYWVTJNS8uY2N+GfdMSePyk5MVUuSYuT1uZmbO5OXMlymsLqRPdB8u\n7385k5MnE+AKoKSmhI3FG9lQtIENxRsI8AugT3Qfekf3pnd0b/UmNwOv17A+r4yvN+9h0eZC1uSU\nUFhhA7TjQHKMXRwoITKIhMhg4iODSYgIotbjJbu4iuziyvrrKrxeQ7eOoXSPCSWpYyjdY8JI7RRB\nt46hPn6XLc/nOz7njoV3EOwfzE3DbmJar2larltaBAVmkaO0LKuI617KoNbj5alLhjG+T5yvS5JW\nrOG8t2O7juXy/pczqtMofQBrYYwx5JfWsDa3hMzcUjJ3lZKzt4r80mr2lNXgPehPZIfQABKjQ0js\nEGpX7SyqYHthJWXVB1brHNc7litOTubUvvGaKaeBrXu38sfFf2TVnlWkxaRx+8jbGRo/1NdlSTun\nwCxyFN5fmcOtb62ia3QIz12eTs+4cF+XJK3Yd3nfceuXt1JZV8mfx/yZM5PP9HVJcgw8XkNheQ15\npdUE+vvRtUPIIedVN8awt9LN9qJKvt60h5eX7CCvtJqkjqH88qTunJ/ejaiQQ8/HboyhuNLNrpIq\nCstrGZzYgajQtjt3uzGG2dtm81jGY+yu3M3klMn8Yfgf1KYhPqPALNIIxhieWrCZv83fyMiUjjwz\nYzjRYYG+LktaKWMML2W+xKMZj9ItohuPT3icnh16+rosOcHcHi/z1+Yzc3EWS7OKcBwIDXARGuRP\nWKCL0EB/Av39KKyoIb+0htq6A6s9hgS4uCA9kSvHpJAcG+bDd9G8Kt2VPL/meV5Y+wIODjcNv4mL\nUy9Wm4accArMIkBJpZsVO4sJcPkxpFsHwhqc0FNb5+XOWat5OyOb6UO78pefDyTIXzNgyLEprS3l\nz9/8mblZczkt6TTuH3M/4YE6UtHerc0t4ZP/396dx7dZnYke/x1JtuRN3rfYjh07sZ3Y2XeyAGFL\nYGhgoMwUKO0wU0q3Cf0A0zJDe9sO3LZMaWnnTm/nli60ZUoLtFDKFgKBhKzOnnh34n2Td8mL9nP/\nkGIcSEwCiSXbz/eT96NX72Id6YmkR+c9S0Ung04vQ24fw24vQy4fLq+PpJhIMqwWMuItZFgtxFpM\nvHikjRePtOL1a66em84/rZ3FillJU7YpT9tgG4/ue5QdLTtYmbGSf1/z72TGZoa6WGIakYRZTDse\nn5/67iGONPVzsLGPg0191NkGR/cbDYp5mVaW5iayNDeR3+9vYvfJHu67eg5brpozZb+QxKWltea1\nhtd4rOwxep29fGXxV/jH0n+U/0/iI7PZnfx2byO/29tI37CH1fnJPHJz6ZRtKqa15k+1f+Kxsscw\nKAMPrXyIG/NvlPeQmBCSMIspSWvNwIhntJd6nc1BdecgtZ0OTnYN4vEF/j/HR0WMJsaLZybg9vo5\n2NhHWUMvR5r7cXr8RBgVj926gJsXy6x84qNptDfy6N5H2dO+h5LkEr6x+huUJJeEulhiinB6fPyh\nrJnHt1bj9Pj5whUFfOGKAiwRU/NKWLOjmYfffZhDtkNsyNnA11Z8jRmxM0JdLDHFScIsJj2H08OR\n5nLwznYAACAASURBVEBt8bGWAZp7h2nrH2HI7TvjuOzEKIrS45iTHkdheiwLshPIT4k554xeHp+f\nijY78VERU7qNoLh03D43vzjxC5489iSRxkj+eck/c1vhbRgNUzOREaHV5XDxyMsVvHikjfyUGB65\nqZTLZqeEuliXhM/v47cVv+Unh3+CX/u5NvdaPlPyGUpS5IeouDQkYRZhxefXuLw+zCbjB4ZZcjjf\nqzFu7h3mZNcgBxv7qO50oHVgbNTCtDhyk6PJSowiKyG4JEaRnxorEw2ICbWvfR+P7H2EBnsDG/M2\n8uDyB0mLTgt1scQ0sLO2i4dfOEFjzzC3LMnmmzfOO+foG5Ndx1AHT1c+zbM1zzLkGWJ5xnI+W/JZ\n1matlY6B4qKShFmEVM+giyPN/Rxq6uNwUz9Hm/tHa4YjjAqLyYg5woDHF2hiMVas2cTimQksmZnI\nsrxEFuUknHUoJyEmUvdIN48feJy/nvor2bHZPLzqYdZkrQl1scQ04/T4+M+3avnZO6dIjTXzvVvm\nc0XR1P3B5nA7+FPtn/htxW/pHO4k3hzP8vTlLM9YzoqMFRQkFFz0ts5+7afX2UvncCedQ510DnfS\nNdxFdEQ0qVGppEankhaVRmp0KtZIq7S1nuQkYRYTwu0NdLSr6rBT3eGgptNBVYeDlr4R4L2Odotn\nJjAjIQqXx4/T68Pp8eH0+DEaIDsxmpzE6MBkAIlRJMVEygeQCBt+7ee5mud44tATjHhHuLv0bj43\n/3NYTJZQF01MY8da+rn/j0eptQ3yqRU5/NsN86b01TaP38ObTW+yq3UX+9v3j04rn2xJZuOsjdyz\n4B6SLEkX/Hcdbgc1fTVU9VZR3VtNVW8VJ/tP4va7zzjOoAz4tf8D52fEZLAyYyUrMwOLXG2afCRh\nFpeU0+Pj17sb+On2OuzBGa5MBkV+agyF6XGUZsWzZGYi87PiiYqUdp1icqrureY7e7/Dsa5jLM9Y\nzsOrHiY/Pj/UxRICCHwO/2hbDT/fcYrM+Cj+49YFU7Zt8/u1OFoo6yhjd9tu3mh8gyhTFJ9b8Dnu\nmHsHZqN53HM7hzp5o/ENtjZu5bDt8Oj2JEsSRYlFFCYWkhWXRXp0Oukx6aRHp5NkScLpddI90k3X\nSBddw110DndytOso+zv2M+AaACA/Pp+l6UspTSmlJLmEgoQCTIap+0NmKpCEWXyoEbePl462sftk\nN26fH49P4/X58fo1JoNiZX4yG4rTmJMWO1rj6/Nr/nSohR++UUP7gJMri1K5aXEWhelx5KfGyDjG\nYkoY9gzz0yM/5XeVv8MaaeWB5Q/IMFcibB1s7OOBZ49S3z3EXatz+fqmYqIjp0+Sdqr/FD88+EPe\naXmHGTEz2LJkCxtnbcTn99Hv6qfP1Ue/s5/qvmq2NmzlSNcRAOYkzuHqmVczP2U+RUlFpEalfqT3\nuF/7qe6tZl/7PvZ27OWY7RgOjwMAi9FCcVJxIIFOKaE0uZSZ1pnSDjuMXNSEWSm1EfgxYASe1Fp/\n7337i4FfAUuAf9Na/+B8zz0bSZg/voERD/YRD5nxFkzGM9+YdbZBnt7XyPMHW7A7vaRbzcRZIjAZ\nFBFGA0aDYsjlpTY4hnFWQhQbitMomWHl17sbqOpwsDA7nq9vmsvqguRQPD0hLgmtNW81v8V3932X\nzuFObplzC19d+lXizfGhLpoQ4xpx+3js9Sp+tauB3ORofvDJhSzPu/AmCpPZ3va9PH7gcap6q4gy\nRTHiHfnAMYWJhVybey3X5l3LrPhZl6Qcfu2nyd5EeU85J7pPUN5TTmVPJU6fE4C4iDjmJc9jfup8\nlmcsZ3HaYqJMUZekLOLDXbSEWSllBGqAa4AWoAz4lNa6YswxaUAucBPQdzphPp9zz0YS5o9Ga82B\nxj6e3tvIK8c7cPv8GA2KzHgL2YlR5CRG09I3wp5TPUQYFRtLM7lz5cxzziLVPjDC9qou3qqysauu\nmxGPj9zkaB68rogb5mdKbZuYErx+Lye6T7CnbQ/vtr7Lse5jzEmcwzdWfYPFaYtDXTwhLsjeUz08\n+NxRWvpG+Mc1s3jguqIpO27z2fi1n5dPvUx5TzkJ5gSSLEkkmBNItCSSGZNJdlxoxt33+r2c7D9J\nRU8FJ7pPcKLnBDW9NXi1F5PBxIKUBazMXMmy9GXkJ+STbEmW79gJcjET5tXAt7TW1wXvPwSgtf7u\nWY79FjA4JmE+73PHkoT57DrtTt6t7UYDVosJa1QEVksE0ZFG3q628fS+Jmptg8SZTfztkiyKM620\n9o3Q3DccHLZtGEuEkduW5XDbshxS48Zv5zWW0+OjtnOQoow4Ik1yKUlMblprtjZu5bX619jXvg+H\nx4FCUZJcwvX51/P3xX9PhEFGZhGT05DLy/9+pZKn9zWRnxrDv28uZc00ads8mQx7hjlsO8y+jn2U\ntZdR0Vsx2rEwyhRFTlwOOXE55Fpz+UTBJyhIKAhxiaem802Yz6eRUxbQPOZ+C7DyPMvxcc6d9rTW\nVLTbebPSxrbKTo61DIx7/MLseB67ZQF/szDzordfs0QYmZ8tl6XF5DfsGeaRvY/w0qmXSI9O55q8\na1g9YzWrMlaRYEkIdfGE+NhizCYevXk+G0sz+Lc/n+COJ/dxw4JMHr5hLpnxcuk/XERHRLMma83o\n8JR2t53jXcdpcjTRZG+i2dFM/UA977S8w69O/Irr86/n3gX3khefF9qCT1Nh0ytAKXUPcA/AzJkz\nQ1ya0OkbcrPrZDfv1nazo6aLtgEnSsHinAQevK6IDcVpxJpNo22U7U4PdqeXeZlWSrMkoRViPHV9\nddz/zv3UD9TzxUVf5J7598jsfGLKWjcnla1fXc9/v3OKn75dx/YqG/981RzuXjNLrhSGIWukNZBA\nc+b47n3OPn5d/mt+X/V7Xqt/jb/J/xvuXXhvyJqXTFfSJCPEXF4fhxr72Vnbxbt13RxvHUBriLOY\nuKwgmauK07myOO2Cmk8IIT7oxboXeWTvI8RExPD99d9nZaZc7BLTR3PvMN9+qYJtlZ3MTovlib9b\nJJUsk0z3SDe/PPFL/lj9R3x+H/9Q+g98cdEXZdi6j+litmE2Eei4dxXQSqDj3u1a6/KzHPstzkyY\nz/vcsaZywuzza052DbKztpt3a7vYe6qXEY8Po0GxZGYCa2ensq4whQVZ8R8Y3UIIceGGPcN8d/93\neaHuBZZnLOex9Y+REiXtOcX09FZVJ//6pxP0Drv51o0lfGpFjnQum2RswzZ+fOjH/OXkX1ictpjH\n1j9GRkxGqIs1aV3sYeWuB54gMDTcL7XWjyql7gXQWv9MKZUBHACsgB8YBOZpre1nO/fDHm+yJ8x+\nv6a8zc67dd3UdDrocrjoHgwsPUNuTr/k+SkxrJ2Twro5qazKT5Lpn4W4yI52HeVfd/4rzY5m7llw\nD19Y+AVpgiGmvZ5BF/f94Qg7a7u5eXEWj9xUSswUniVwqnr51Mt8Z893iDBG8OiaR7k85/JQF2lS\nkolLJpjN7uStKhs767rZXddN37AHCIxhnBpnJjXOTEqsmdTYSLKTormsIJnsxOgQl1qIqcnr9/Lz\nYz/nv4/9N+nR6Ty69lGWZXzo56EQ04bPr/mv7XX8aFsNBamx/N87ljAnPS7UxRIXqGGggQd3PEhV\nbxV3zbuL+5bcR4RRKt8uhCTME8Tp8fHzHaf4r7frcHr8pFvNrJmdwro5KayZnUJanCXURRyX2+em\ndbD1kg3gLsREa7I38dDOhzjWfYwb82/koZUPERcpiYAQZ7OrrpstzxxmyOXj06tzuXNlLjOTpTJn\nMnH5XPxH2X/wh+o/MDthNg+teIgVmStCXaxJQxLmi0BrTc+Qm4SoiLO2J95eZeNbL5XT2DPMptIM\n7ru6kML02EnTHszpdfLlN7/Mvo59fGnRl/j8gs9PmrIL8X6tg638ruJ3PF/7PCaDiW+u+iYbZ20M\ndbGECHuddiffeamC18o78GvNlUVpfHp1LpfPScVgkO+EyWJ703a+t/97tA21cU3uNdy/7H6yYrNC\nXaywJwnzR9Ax4ORoSz/HWwY43hpYeofcmE0G5mZaKZkRGLotNzmaX77bwLbKTvJTY/j2J0pYNyd1\nwsv7cbh9brZs38Ku1l0sy1hGWUcZ1+ZeyyNrH5EpOsWkUt5TzlMnnmJr41YUio2zNrJlyRbpBCPE\nBWofGOH3+5v5/f4muhwucpOjuXNlLp9clk1CdGSoiyfOg9Pr5Knyp/jFiV/g134+W/JZ7i69m+gI\nuWpwLpIwnwe/X3OsdYA3KjrYVmGjutMBgNGgmJMWy4LseArT42gfcFLeNkB5mx2H0wtAdKTxjPEs\n7W47dpcdgzJgUAYUCqUUcZFxYZeAev1eHnjnAd5sepNvrv4mt865lV+V/4onDj5BcVIxP9nwE0k2\nRFgb8Y6wvWk7z9c+z/6O/cRExPDJwk9yx9w75P+uEB+T2+vn9fIOfrunkf0NvZhNBjYvmsFdq/Nk\nKLpJomOogx8e/CGv1r9KWlQaX1z0RTbP3ixD0J3FlE6Y7U4Pu+u6UUoxN8NKdmLUGZeNvD4/h5v7\n2V5l460qGy19IyTHRpISayYlNpLUODMer2Z7tQ2bw4XRoFiel8jVc9NZkpvIvEwrlogP9qTXWtPc\nO8LR1naMUS20O+uo6KmgoqeClsGWs5bVoAzkWnMpTioeXfKseaOJ9enk2qiMRBojsZgsGNR7zT9G\nvCO0DbbROthKi6OFzuFOihKLWJe97iO1y/T5fTz07kO8Wv8qX1v+Ne6cd+fovh0tO/iXHf+CxWjh\niSufYGHqQmmiIcKGz++jrLOMl06+xLbGbQx7h8mMyeT24tu5pfAWaacsxCVQ2W7nt3sb+fOhVkY8\nPhbPTOCedflsLM2Q74dJ4LDtMI8feJyjXUeZFT+LLUu2sCFng8RujCmXMJ/qGuStKhtvVtooa+jF\n63+v3LFmE0UZcRRnxGF3etlR08XAiAeTQbEsL5HiDCt9w+7R4d26HC68Ps26whSumZfOlUVp415u\n0lpT3VfNzpad7GzdydGuo6PzvWfFZjEveR7zkueREpWC1hqNxq/9+LWf7pFuKnsrqe6tpn2o/bye\nv8lgwmK0YDQYGXCdOR22QRnwaz8mg4mVGSvZMHMDV+ZcSaIlkX5XP73OXvqcffQ6e1EokqOSA4sl\nmbjIOP7X7v/FC3UvsGXJFv5p/j994LFP9p/kK299hWZHYEZzkzJhMgQWi8nC5dmXc1vRbcxLnnde\nz0WIj6truItna57l+drnsQ3biI2I5Zrca7ix4EaWpi894wemEOLSsDs9PH+whd/saaS+e4hFOQk8\ntKmYlfnJoS6a+BBaa95qfosfH/ox9QP1LExdyP3L7mdx2uJQFy0sTOqEOTG3WK954Oe4vX7cXj9D\nbh9dDhcARelxbJibxobiNEwGRVWHg6p2O5UdDirb7ZhNRq4oSmVDcRpr56RgvcCxjbXWdI90U9df\nx8n+k1T1VrGnbQ+2ERsA85LnsTZrLUvTlzIvaR4JloTz/tv9zn6q+qpoG2wbTagB/NqPT/tw+Vy4\nvK7Arc+Fx+8hPTqdrNgssuKyyIrNItGcyPHu47zV9BZvNr1Jk6PpvB/fpEx4tZd7F97LlxZ96ZzH\nDbgGeKHuBYY8Q3j9Xrx+Lx6/hx5nD9ubtuP0OVmQsoDbim7jurzrsJjCeyQQMTkd6zrG05VPs7Vx\nKz6/jzVZa9hcsJkrcq6Q/3NChIjPr3n+YAs/fKOGDruTq+em8bWNxTIk3STg9Xt5se5Ffnrkp9hG\nbHyq+FPct+S+ad++eVInzMl5c/Xmb/2GSKOBSJMBs8nAgux4rixOG3fsYq31WS8zaK3pHO7kRPcJ\njncfp7y7nCZHEyaDiUhDJJHGyMC4hRoa7A3Y3fbRcxPMCSzPWM767PWszVobVjOEaa052X+St1ve\nxu1zk2RJItGSGLg1J6LR9Dh76BkJLN3ObnLjcvnbOX/7kS/H2N12Xjr5En+o/gP1A/VYI61kxGTg\n8XvOSK4zojNYlLaIhakLWZS26KztSj0+DxpNpFE6k4gAn9/HG41v8JuK33C8+zixEbHcNPsmPlX8\nKWZaZ4a6eEKIoBG3j1/uqudnb59kyO3lhgUzuHFBJusLU8/apFGEj2HPMP95+D/5XeXvmBk3k0fW\nPjKta5sndcL8cTr9eXwe6u311PTVUNtXS01fDVW9VXSPdAOB5g6FiYUUxBfgx4/b5x5d/PiZGTeT\ngoQCZifMpiChgGRLsrT1OQutNQc6D/BC3QsMugcxGUxEGCNGm3A0OZo40X2CEe8IAOnR6aRHp+Pw\nOBh0D+JwO3D6nBiVkfyEfEqSS0abthQmFoZdR0lxaXn9Xl6pf4WfH/s5DfYG8qx53D73dj5R8Ali\nImJCXTwhxDn0Drn5r+11PHewhYERD7FmE1fNTWNTaSZXFEnyHM7KOsr4xq5v0DbYxl3z7uLLi788\nLa/eTYuEWWtNx1AHR7qOcMR2hCNdR6jpq8HrD4xkYTKYKIgvoCipiJLkEuanzKcwqRCz0Xypn4IA\nPH4PNX01HLUd5UjXEfqcfcRFxmGNtBIXGUdsRCwun4uK3goqeyrpdfaOnhttiibBnECCJYFEcyJx\nkXF4/B6cPidOrzPQdMXvIsoUNfr3rJFWrJFWsmKzmJ04m4L4AmIjY0P4CogP4/F5ePHkizx5/Ela\nB1spSizingX3cHXu1dI2WYhJxOPzs/tkD68ca+f1ig76hz3ERBrZMDedG+ZncEVRmiTPYWjYM8zj\nBx7njzV/JNeay51z7+SG/BumVSfqSZ8wl5WVUT9Qz0HbQY51HWPQPYhXe0fb+/r8Pk4NnMI2HGhb\nHGWKYkHKAkpSSihKLKIwsZDc+FwiDDJF5GRwutlMRU8FJ/tP0uvsZcA1QJ+rjwHXAHa3nQhDBBaj\nBYvJgtlkxmwwM+Idwe6243A7Rm992jf6dzNjMpmdMJvkqGQUajQJU0qRHp3OtbnXkp+QH6qnPW3V\n9NXwYt2L/PXUX+l19lKaXMrnF36ey7Mvlys6QkxyHp+fvad6eOV4O6+Xd9I75CY60siG4jQ2lmaw\nclYyqXFScRVOdrfu5keHfkRVbxVRpig2zdrErXNupTSldMp/Jk/qhDm9KF0XfbuIPlcfAEmWJJIs\nSRiVEaPBiFEZMSgDM2JmsChtEYvTFjMncY6MLyjwaz+tg63U9dVxcuAktX211PXXMeAaQKMJ/AuM\nYtLr7EWjmZ0wm415G7ku7zry4vNC/RSmrF5nL6/Wv8qLdS9S2VuJSZm4POdybiu8jdUzVk/5D2Uh\npiOvz8+++l5ePt7O6yc66BlyAzArJYZluYksz0ti+awkZqVI06tQ01pT3lPOczXP8Ur9K4x4RyhO\nKuaOuXdw/azrp2x/o0mdMMcXxOsvP/VllqYvZUnaEnKtufJlKi66ruEutjZu5fWG1zlsOwzA7ITZ\nLEpbxIKUBSxMWzg6Zra4MFprGuwNo02lDtsOUz9QD8DcpLlsnr2ZTbM2kWRJCnFJhRATxevzc7Rl\ngAMNvZQ19HGgsZf+YQ8AC7Lj+bvlOdy4cMYFj24lLr5B9yCv1L/CM9XPUNtXS0pUCrcX385tRbcR\nb55ak9dM6oQ5VFNji+mrY6iDrQ1b2dW2i+Ndx3F4ArM+xkXGUZJcwqz4WeRacwNLXC6ZsZlyRWMM\nl89FeXc5h22HR5Pkflc/ANZIKwtTF7I4bTHrs9dTlFQU4tIKIcKB36851T3IOzXdPHugmaoOB5YI\nAzfMn8HfLc9heV6iVJaFmNaaPe17eKr8KXa37SbKFMXmgs2sy15HcVIxqVGpkz5GkjAL8RH5tZ+G\ngQaOdh3lWPcxKnoqaLQ3MuQZGj3GqIzEm+PP6Gw4um4+sxNilCkKs9FMpDFy9Pb0utloJsIQgdlo\nxmgwonVw0hsC43QbMASGPAwzHp+HI11H2NO2h/0d+ynvKR/tbJtrzWVRaqCp1KK0RcyKnyW19EKI\ncWmtOdYywB8ONPOXI20MurwUpcdx12W53Lw4i+hIqaAItZq+Gn5T/htern959PM+0ZxIUVIRxUnF\nLM9YzqrMVZOu6YYkzEJcRFoHxrRutDfSZG+i2dFMv6v/jM6Go7cuO17tveDHUKhAO+v3sRgto4m4\nNdJKoiWRuUlzWZi2kPkp8y/ZsGs+v48B9wD9rv5AB0xnH62Drext30tZRxkj3hGMykhJSglL05ey\nKHURi9IWSTMLIcTHMuz28tej7Ty1p4HyNjtWi4m/W57Dp1flMTN5ek+yEQ4cbgfVvdVU91WP3tb1\n1eH2u4mNiGV99nquyb2GNVlrJsUQsZIwCxEiWuvR0TvsbntgGDyfa3S8b5c/sH562+mZHX1+HwZl\nOGPx+X2jyfjppWu4iwZ7AxBIsmcnzmZ+ynzMRvPoxDFjJ5E5vT72vsfvwau9eHzB/afX9XvHunyu\nsz6/XGsuqzJXsXrGalZkrJhWww8JISaO1pqDjX38ancDr53owK81K2clcV1JBtfMSx93IjMxsdw+\nN3vb97KtcRvbm7fT7+onyhTF1TOv5q6SuyhOKg51Ec9JEmYhpjC7287xruOBZiNdx6jsrcSnfaMT\nx5gMJiIMEWe9ff9+k8GESZ058UyEIQKzyRwYC3vMkhqdSlp0WqifvhBimukYcPL7/U28crydWtsg\nAKVZVq6dl8ENCzIpSJUx98OF1+/lQOcB3mh4g5dOvcSId4RVmav4TMlnWDNjTdi1eZaEWQghhBBT\nzqmuQbZWdLK1vINDTYHOxcvzErltWQ43LMiU9s5hZMA1wHM1z/E/lf+DbcTG7ITZ3Fp4a2AiucTC\nsJhZUBJmIYQQQkxpnXYnfz7cyh/KmqnvHiLWbOITi2Zw69JsFuckhF1t5nTl8Xl4teFVfl3+a2r7\naoFA5/nZCbOZlzyP0pRSLptxGdlx2RNeNkmYhRBCCDEtaK0pa+jjmbJAsw2nx09WQhSbSjPYND+T\nxTkJGAySPIea1pqOoQ4qeioo7ymnoqeCip6K0Ynqcq25rJmxhjVZa1iWvozoiEvfTl0SZiGEEEJM\nO3anhzfKO3nleDs7a7tx+/xkxlvYWBroLLgiLwmTUYa6DBenJ7ra3babXa27KOsow+lzEmmI5PKc\ny9k0axPrstZdsuYbkjALIYQQYlqzOz28WdnJy8c62FHbhdvrJz4qgg3FaVwzL531hanEmqXNczhx\n+Vwc6jzE281v83rD6/Q4e4iJiOGqmVdxXd51zE6YTUpUylnHe9Za0+/qp2ukixkxM4iN/PDOoJIw\nCyGEEEIEDbm87KztYmtFJ29V2egf9mA0KHISo8hPjWVWSgz5qTEUpMZSMsNKnEzRHXJev5eyjjJe\nrX+VbY3bRmfhBUgwJ5ASlUJKVArDnmG6RrroGukanVQlJiKGm2ffzO3Ft5NjzTnnY0jCLIQQQghx\nFl6fnwONfeyu6+Zk9xCnuoao7x7E6fEDYFBQmB7HktxEls5MZEluInnJ0dKJMITcPjcHOg/QMdSB\nbdhG90g3tmEbPc4eok3RpEWnkRqVSmp0KonmRHa27uS1+tfwaR9X5lzJp+d9moWpC2kbaqPF0RJY\nBlt4YPkDkjALIYQQQpwPv1/TYXdS0+ngSHM/Bxv7ONLUj8MVqLHMT4nhbxbO4MYFmcxJlwmbJgPb\nsI1nqp7hjzV/ZMA18IH9ZqOZg58+KAmzEEIIIcRH5fNr6myD7K/v4dUTHew51YPWUJwRx40LZ7Cx\nNEMmTZkERrwjvFr/Ku1D7WTHZpMTl0N2XDYpUSkYDUZJmIUQQgghLhabw8mrxzt46WgbBxoDQ6HN\nSolhQ3EaV81NY3leEhEyAsekclHbMCulNgI/BozAk1rr771vvwruvx4YBj6rtT4U3NcAOAAf4D2f\nQknCLIQQQohw1tY/wpuVnWyrtLHnZA9un584i4mNJRn807p8ijKk2cZkcNESZqWUEagBrgFagDLg\nU1rrijHHXA98hUDCvBL4sdZ6ZXBfA7BMa919voWXhFkIIYQQk8WQy8u7dd28UdHJy8faGfH4WF+Y\nyufWzWLt7BTpLBjGzjdhPp/rBiuAOq31Ka21G3gG2Py+YzYDv9EBe4EEpVTmBZdaCCGEEGKSiTGb\nuK4kgx98ciF7HtrAg9cVUdFm59O/2M+mH+/k+YMtuL3+UBdTfAznkzBnAc1j7rcEt53vMRrYppQ6\nqJS651wPopS6Ryl1QCl1oKur6zyKJYQQQggRXhKiI/nSlbPZ9fUreezWBfi15v5nj7Lusbf4v2+f\nZGDEE+oiio9gIlqmr9VaLwI2AV9SSq0/20Fa6/+ntV6mtV6Wmpo6AcUSQgghhLg0zCYjty3L4fX7\n1vPrf1jOnLQ4vv9aFZd9902+/VI5zb3DoS6iuADnMx9kKzB2ipTs4LbzOkZrffrWppT6M4EmHjs+\naoGFEEIIISYLpRRXFKVxRVEa5W0D/GJnPb/d08ivdzdwWUEyf7s4m42lGcTIFN1h7Xw6/ZkIdPq7\nikASXAbcrrUuH3PMDcCXea/T30+01iuUUjGAQWvtCK6/AXxHa/3aeI8pnf6EEEIIMVV1DDh5pqyJ\nPx9upbFnmKgII5tKM7hpcRYr85Mwm4yhLuK0cb6d/j7054zW2quU+jLwOoFh5X6ptS5XSt0b3P8z\n4BUCyXIdgWHl/iF4ejrw52DvUBPwPx+WLAshhBBCTGUZ8Rbuu7qQLVfN4WBjH88fauWvx9r40+FW\nzCYDS2Ymsio/mVX5SSyamSAJdBiQiUuEEEIIIULM6fGxs7abvad62Huqh4p2O1qD2WTgsoJkrpqb\nztVz08mIt4S6qFPKRZ24ZKJJwiyEEEKI6Wxg2ENZQy+7TnbzZqWNpmAnwdIsK1fPTefKojRKs+Ix\nGmSM549DEmYhhBBCiClAa02dbZBtlTa2VXZyqKkPrSExOoK1c1JZNyeFywtTSbdK7fOFkoRZju0e\n1wAACvdJREFUCCGEEGIK6hl08W5dN+/UdLGztpsuhwuAzHgLM5Oi31uSo0m3WrBaIoizmLBaIoi1\nmKRWeoyL1ulPCCGEEEKEj+RYM5sXZbF5URZaa6o6HOys7aKqw0FTzzDv1HRhCybRZ5MUE8mctFjm\nZlopyogLLOlxMrTdOOSVEUIIIYSYpJRSzM20MjfTesb2EbeP5r5hbHYXDqcHh9OLPXhrczip6nDw\n7IFmhty+0XNykqIozrBSHEyiizOszEqJkRppJGEWQgghhJhyoiKNFKbHUZged85j/H5NS98IVR12\nqjscVHU6qGq382ZlJ/5gi93oSCPzMq2UZsVTmhXP/Kx4ClJjMBknYrLo8CFtmIUQQgghxCinx0ed\nbZDKdjvlbXZOtA5Q3mZnxBOojbZEGJibaaV0RiCBLs2KpygjblLWREunPyGEEEIIcVH4/Jr67kGO\ntw5wvMXOibYBKtrsDLq8AKTFmdm8aAY3L85m3gzrh/y18CEJsxBCCCGEuGT8fk1DzxBHW/p5+VgH\nb1fb8Po1xRlx3LQ4i40lGeQmRxOc8TksScIshBBCCCEmTO+Qm5eDU3wfbuoHIMNqYcWsJFbMSmJV\nfhIFqbFhlUBLwiyEEEIIIUKisWeIHbXd7K/vZd+pntFh7mLNptFxonOTo8lJiiY7MYp0q4V0q4XE\n6IgJTaglYRZCCCGEECGntaaxZ5h99T1UtNlp6h2mqXeY5r4R3F7/GcdGGBWpsWZS48zEWkxER5qI\nNZuIjjQSazGRFB1JSqyZ5NjAbUrw2I/a4VAmLhFCCCGEECGnlCIvJYa8lJgztvv9mk6Hk5a+EWx2\nFzaHk87gbfegmyGXl57BYYbcXoZdPhwu7wcSbACTQZGZYCE7IVBbnZ0YTXyUiQiTgQijAXPw1qAU\npyuvLzS9loRZCCGEEEJMOINBkRkfRWZ81Hkdr7VmyO2j2+GiZ8hFl8NN16CL9v4RWvpGaO0fYUdt\nF532c89y+FFJwiyEEEIIIcKeUopYc6CJxvtrq8dyeX0Mu3x4fH5cXj8enx+PT+P1B2qnx7ZGnv/9\n83tsSZiFEEIIIcSUYTYZMZuMF/VvTq95DYUQQgghhLhAkjALIYQQQggxDkmYhRBCCCGEGIckzEII\nIYQQQoxDEmYhhBBCCCHGIQmzEEIIIYQQ45CEWQghhBBCiHFIwiyEEEIIIcQ4JGEWQgghhBBiHJIw\nCyGEEEIIMQ6lx06oHSaUUg6gOtTlEGeVAnSHuhDirCQ24UtiE74kNuFLYhO+plJscrXWqR92kGki\nSvIRVGutl4W6EOKDlFIHJDbhSWITviQ24UtiE74kNuFrOsZGmmQIIYQQQggxDkmYhRBCCCGEGEe4\nJsz/L9QFEOcksQlfEpvwJbEJXxKb8CWxCV/TLjZh2elPCCGEEEKIcBGuNcxCCCGEEEKEBUmYhRBC\nCCGEGMeEJMxKqV8qpWxKqRNjti1SSu1VSh1RSh1QSq0Ibo9QSj2llDqulKpUSj005pylwe11Sqmf\nKKXURJR/KjtHbBYqpfYEX+uXlFLWMfseCr7+1Uqp68Zsl9hcZBcSG6XUNUqpg8HtB5VSG8acI7G5\nyC70fRPcP1MpNaiUemDMNonNRfYRPtMWBPeVB/dbgtslNhfZBX6mSS4wgZRSOUqp7UqpiuB7YUtw\ne5JS6g2lVG3wNnHMOdMrH9BaX/IFWA8sAU6M2bYV2BRcvx54O7h+O/BMcD0aaADygvf3A6sABbx6\n+nxZLnpsyoDLg+t3A/8eXJ8HHAXMwCzgJGCU2IRFbBYDM4LrpUDrmHMkNiGMzZj9zwHPAg9IbMIj\nNgTmIjgGLAzeT5bPtLCJjeQCExubTGBJcD0OqAl+5z8GfD24/evA94Pr0y4fmJAaZq31DqD3/ZuB\n07/y44G2MdtjlFImIApwA3alVCZg1Vrv1YGI/Aa46ZIXfoo7R2wKgR3B9TeAW4Lrmwl8gLm01vVA\nHbBCYnNpXEhstNaHtdan30PlQJRSyiyxuTQu8H2DUuomoJ5AbE5vk9hcAhcYm2uBY1rro8Fze7TW\nPonNpXGBsZFcYAJprdu11oeC6w6gEsgi8L3/VPCwp3jvtZ52+UAo2zDfB/yHUqoZ+AFw+nLLc8AQ\n0A40AT/QWvcSCFzLmPNbgtvExVdO4M0A8EkgJ7ieBTSPOe50DCQ2E+dcsRnrFuCQ1tqFxGYinTU2\nSqlY4GvAt993vMRm4pzrfVMIaKXU60qpQ0qpfwlul9hMnHPFRnKBEFFK5RG4arkPSNdatwd3dQDp\nwfVplw+EMmH+AvBVrXUO8FXgF8HtKwAfMINANf/9Sqn80BRx2rob+KJS6iCBSzPuEJdHvGfc2Cil\nSoDvA58PQdmmu3PF5lvAj7TWg6EqmDhnbEzAWuCO4O3NSqmrQlPEaetcsZFcIASCP/CfB+7TWtvH\n7gvWGE/bsYhNIXzszwBbguvPAk8G128HXtNaewCbUmoXsAzYCWSPOT8baJ2gsk4rWusqApcqUUoV\nAjcEd7VyZo3m6Ri0IrGZEOPEBqVUNvBn4C6t9cngZonNBBknNiuBW5VSjwEJgF8p5STwpSSxmQDj\nxKYF2KG17g7ue4VAG9vfIbGZEOPERnKBCaaUiiDwufS01vpPwc2dSqlMrXV7sLmFLbh92uUDoaxh\nbgMuD65vAGqD603B+yilYgg0HK8KXhKwK6VWBXtc3gW8OLFFnh6UUmnBWwPwMPCz4K6/AH8fbBs7\nC5gD7JfYTJxzxUYplQC8TKBzxq7Tx0tsJs65YqO1Xqe1ztNa5wFPAP9ba/1/JDYTZ5zPtNeB+Uqp\n6GBb2cuBConNxBknNpILTKDga/kLoFJr/cMxu/5CoIKT4O2LY7ZPr3xgInoWAr8n0A7JQ+AX/T8S\nuPx1kEAvy33A0uCxsQRqnMuBCuDBMX9nGXCCQG/M/0NwpkJZLnpsthDoIVsDfG/s6wz8W/D1r2ZM\nz1eJTWhjQ+CLZgg4MmZJk9iEPjbvO+9bnDlKhsQmxLEB7gx+35wAHpPYhEdsJBeY8NisJdDc4tiY\n75DrCYwc8yaBSs1tQNKYc6ZVPiBTYwshhBBCCDEOmelPCCGEEEKIcUjCLIQQQgghxDgkYRZCCCGE\nEGIckjALIYQQQggxDkmYhRBCCCGEGIckzEIIIYQQQoxDEmYhhJhmlFLGUJdBCCEmE0mYhRAijCml\nvqOUum/M/UeVUluUUg8qpcqUUseUUt8es/8FpdRBpVS5UuqeMdsHlVKPK6WOAqsn+GkIIcSkJgmz\nEEKEt18SmF729PTBfw90EJiKdgWwCFiqlFofPP5urfVSArNt/bNSKjm4PQbYp7VeqLV+dyKfgBBC\nTHamUBdACCHEuWmtG5RSPUqpxUA6cBhYDlwbXIfANMJzgB0EkuSbg9tzgtt7AB/w/ESWXQghpgpJ\nmIUQIvw9CXwWyCBQ43wV8F2t9X+PPUgpdQVwNbBaaz2slHobsAR3O7XWvokqsBBCTCXSJEMIIcLf\nn4GNBGqWXw8udyulYgGUUllKqTQgHugLJsvFwKpQFVgIIaYSqWEWQogwp7V2K6W2A/3BWuKtSqm5\nwB6lFMAgcCfwGnCvUqoSqAb2hqrMQggxlSitdajLIIQQYhzBzn6HgE9qrWtDXR4hhJhupEmGEEKE\nMaXUPKAOeFOSZSGECA2pYRZCCCGEEGIcUsMshBBCCCHEOCRhFkIIIYQQYhySMAshhBBCCDEOSZiF\nEEIIIYQYhyTMQgghhBBCjOP/A2ep3Qx7jJCvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x175bcc0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boy names that became girl names (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:05:05.911716Z",
     "start_time": "2019-01-19T04:05:05.851687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False ..., False False False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = top1000.name.unique()\n",
    "mask = np.array(['lesl' in x.lower() for x in all_names])\n",
    "print(mask)\n",
    "lesley_like = all_names[mask]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:05:58.254140Z",
     "start_time": "2019-01-19T04:05:58.196343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:06:19.749372Z",
     "start_time": "2019-01-19T04:06:19.701958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "      <th>extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>79</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2462</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>92</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>9</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop  extra\n",
       "632   Leslie   F       8  1880  0.000088      0\n",
       "1108  Leslie   M      79  1880  0.000715      0\n",
       "2462  Leslie   F      11  1881  0.000120      0\n",
       "3014  Leslie   M      92  1881  0.000913      0\n",
       "4512  Leslie   F       9  1882  0.000083      0"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:12:52.341777Z",
     "start_time": "2019-01-19T04:12:52.295359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>8.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>11.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>7.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>15.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F      M\n",
       "year             \n",
       "1880   8.0   79.0\n",
       "1881  11.0   92.0\n",
       "1882   9.0  128.0\n",
       "1883   7.0  125.0\n",
       "1884  15.0  125.0"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "table.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:12:52.909004Z",
     "start_time": "2019-01-19T04:12:52.868829Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880     87.0\n",
       "1881    103.0\n",
       "1882    137.0\n",
       "1883    132.0\n",
       "1884    140.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.sum(1)[:5] # 沿列求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:15:05.792856Z",
     "start_time": "2019-01-19T04:15:05.499379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table = table.div(table.sum(1), axis=0)  # 沿行做除法\n",
    "new_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:15:08.449877Z",
     "start_time": "2019-01-19T04:15:08.414932Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-19T04:15:09.351799Z",
     "start_time": "2019-01-19T04:15:09.014768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17602b610>"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OFHv27LGKixj//vtvsbOzk549e2odxeLi4uKkfPnyYm9vbyzE48aNk+vXr2e7f0ZGhkRH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\nYiyEEEIIIQQASYyFEEIIIYQAAPx/HjxMc2vgrqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17722e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_table.plot(style={'M': 'k-', 'F': 'k--'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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